Trade and Environmental Assessment Model. Model Description. September 29, 2009

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1 Trade and Environmental Assessment Model Model Description Cambridge, MA Lexington, MA Hadley, MA Bethesda, MD Washington, DC Chicago, IL Cairo, Egypt Johannesburg, South Africa September 29, 2009 Prepared for National Center for Environmental Economics / Climate Economics Branch U.S. Environmental Protection Agency 1200 Pennsylvania Avenue, NW Washington, DC Under Contract: EP-W Work Assignments #1-42/2-42/3-74/3-87 Abt Associates Inc. 55 Wheeler Street Cambridge, MA 02138

2 Contents Chapter 1 : Introduction Objectives Overview of the TEAM Analytical Framework TEAM Development History Document Organization...6 Chapter 2 : General Data and Analytical Framework Using an Input-Output Analysis Framework to Understand the Relationship Between Pollutant Releases and Economic Activity Using a Linear, Fixed Coefficients Production Model to Estimate Changes in Pollutant Releases in Relation To Changes in Economic Activity Using the North American Industrial Classification System (NAICS) as the Economic Framework for Organizing Data and Analysis in TEAM Using a Geographical Framework Based on the State or Observed Production Entity Using Individual Categories of Pollutant Release and Resource Use as the Framework for Understanding Primary Environmental Impacts Chapter 3 : Baseline Economic Activity Data Economic Census Data National-Level Economic Data State Data Agricultural Census National Data State Data Overview of TEAM Economic Baseline Summary of TEAM Economic Baseline Datasets Comparison of TEAM Economic Baseline with Revenue Reported in Census NERC-Adjusted Economic Baseline Chapter 4 : Emissions Baseline Data Water Discharges PCS Data TRI Water Dischargers Data Air Emissions Point Source Air Emissions Area Source Criteria Air Emissions Abt Associates Inc., September 29, 2009 Contents i

3 4.2.3 Mobile Source Air Emissions Energy Use and Carbon Dioxide Emissions Compilation of Energy Use Data Carbon Emissions Integrating Energy Use and CO 2 Emissions Data into TEAM Data Limitations NERC-Adjusted Emissions Baseline Data Chapter 5 : Assessing the Environmental Impacts of Economic Changes Trade or Economic Event Description Converting Economic Sector Classification Systems into NAICS Converting the Base-Year to 2002 Dollars Converting Primary Economic Impacts into Total Impacts Calculating Changes in Emissions/Resource Use Disaggregating Economic Event Impact Values to the State Level Calculating Changes in Emissions/Resource Use by Multiplying Economic Event Impact Values by Emission Factors Compiling and Reporting Results Validation Chapter 6 : Interpreting Environmental Impacts Human Health and Welfare Risk-Related Results Getting from Hazard to Risk Environmental Baseline and Ambient Conditions Baseline Water Quality Data Baseline Air Quality Data Informing TEAM Analysis using the Environmental Quality Baseline Expected Effects of Emissions on Ambient Conditions Chapter 7 : Assumptions and Key Uncertainties Imports vs. Domestic Production Scalability of Environmental Impacts Spatial Distribution of Economic Changes and Environmental Impacts Sector Mapping Environmental Impacts not Reflected in TEAM Results Chapter 8 : Modeling Interface Abt Associates Inc., September 29, 2009 Contents ii

4 8.1 System Requirements and Input/Output Files Run Time Considerations TEAM Execution Module Input/Output Files TEAM Reporter Chapter 9 : References Appendix A Development of TEAM Support Files A.1 Concordances between Sectoral Frameworks A.1.1 Introduction A.1.2 Trade or Economic Event Concordance Tables A.1.3 Limitations of Concordance Tables A.1.4 References A.2 PPI Adjustment A.3 Total Requirements Coefficients A.3.1 Developing the Input-Output Matrix A.3.2 NAICS Sectoral Redefinition A.3.3 Total Requirements A.3.4 Import-Adjusted Total Requirements A.3.5 NERC-Adjusted Total Requirements A.3.6 Final Data Sets and Considerations Appendix B Supporting Material for TEAM Economic Baseline Appendix C Supporting Material for TEAM Emissions Baseline C.1 Area Source Air Emissions C.2 Mobile Source Air Emissions C.3 Energy Use Appendix D Energy Consumption and Carbon Emissions Adjustments D.1 Separating Transportation Energy Consumption and Emissions into Value Added and Non- Value Added Components and Passenger vs. Freight Components D.1.1 Assign Transportation Modes into Relevant Categories: (1) Value Added vs. Non- Value Added and (2) Passenger vs. Freight D.1.2 Calculate National Shares of Individual Transportation Fuels by Disaggregation Category D.1.3 Calculate State-Level Energy Consumption and Carbon Emissions by Fuel Type and Disaggregation Category D.2 Primary Consumption versus End-User Consumption in the Electric Power Sector Abt Associates Inc., September 29, 2009 Contents iii

5 D.2.1 D.2.2 Accounting for Electric Power Sector Emissions on a Primary Consumption or End- User Consumption Basis Adjusting the Emission Factor Used to Assign Carbon Emissions from Electric Power Generation to End Use Sectors D.3 Adjusting for Non-Fuel Use of Energy Inputs D.3.1 Fuel-Specific Discussion/Implementation of Non-Fuel Use Adjustment: Storage and Total Non-Fuel Use by State D.4 Adjusting for Incomplete Combustion of Energy Inputs Appendix E Review of Test Case and Results E.1 Processing Economic Event Data for the Test Case E.2 Converting Test Case Economic Event Data to NAICS Framework E.3 Converting NAICS Framework Economic Event Data to 2002 Dollars E.3.1 Converting Primary Impact Economic Event Data to Total Impact Data E.4 Emissions/Resource Use Results E.4.1 Industry Impacts E.4.2 Regional Impacts E.4.3 Industry Impacts Using Import-Adjusted I-O Framework E.5 Validating TEAM Execution Appendix F Estimation of Emission Factors F.1 Introduction F.2 Calculating Emission Factors F.2.1 Methodologies for Developing Emission Factors F.2.2 TEAM Emission Factors F.3 Use of Emission Factors in an Input-Output Framework F.4 Selected Examples of Environmental Input-Output Analyses F.5 References Abt Associates Inc., September 29, 2009 Contents iv

6 Tables Table 2-1: Input-Output Relationships for Three Sectors in a Hypothetical Economy...9 Table 2-2: NAICS 2-digit sectors...13 Table 2-3: NAICS framework, as implemented in the 2002 Economic Census...14 Table 3-1. Sectors Outside the Scope of the 2002 Economic Census...17 Table 3-2: Excerpt from 2002 Economic Census Economy-Wide Key Statistics State Data Set...19 Table 3-3. Revenue flags from economic census geographical area series tables...21 Table Economic Census data for Utility Sector (221)...23 Table 3-5. Economic sectors covered in the 2002 Census of Agriculture...26 Table 3-6. Incidence of estimated revenue values in TEAM state baseline file (sectors reported in Census of Agriculture...27 Table digit NAICS Sectors for which Revenue Values Could not be Estimated for TEAM...28 Table 3-8. Comparison of Reported National Revenue and the Sum of Estimated/Reported State Values, by 2-digit NAICS sector...29 Table 4-1: Parameters excluded from TEAM baseline data selection...34 Table 4-2: Summary of PCS Direct Water Discharge Data for Use in TEAM Table 4-3: Summary of TRI Indirect Water Discharge Data for Use in TEAM...40 Table 4-4: Summary of TRI Direct Water Discharge Data for Use in TEAM Table 4-5: NEI Components and their Respective Pollutants...44 Table 4-6: Amount of Point Source Air Emissions by Reported Classification Codes...45 Table 4-7: Amount of Point Source Air Emissions Omitted During Data Compilation...46 Table 4-8: SCC Codes with Highest Occurrence Frequency...47 Table 4-9: Example of the SCC-NAICS Concordance...49 Table 4-10: Pollutants Eliminated Entirely...50 Table 4-11: Vehicles and Corresponding Fuel Type...52 Table 4-12: SCC Categories with Highest Levels of Off-Highway Emissions...54 Table 4-13: SCC Categories with Highest Levels of Highway Emissions...55 Table 4-14: Example of the SCC-NAICS Concordance...56 Table 4-15: Pollutants Eliminated Entirely from the Master Off-Highway Emissions Dataset...57 Table 4-16: HPMS, VIUS and NEI Vehicle Classifications...60 Table 4-17: VIUS Industry Groups...61 Table 4-18: NAICS Industry Sectors with Highest Levels of Emissions...61 Table 4-19: Pollutants with Highest Levels of Emissions...62 Table 4-20: Comparison of Energy Consumption (Fuel-Use) for 4-digit NAICS and 3-digit NAICS Abt Associates Inc., September 29, 2009 Contents v

7 Table 4-21: Concordance between SEDS and NAICS sectors Table 5-1: Reporting Elements By Pollutant/Resource Category in TEAM...80 Table 6-1: Most Often Stated Causes of Impairment in 303(d) List...84 Table 6-2: Most Often Stated Causes of Impairment in Section 305(b) List...85 Table 6-3: Nonattainment Status Statistics...87 Table 6-4: Peak Air Quality Statistics and Applicable NAAQS...88 Figures Figure 2-1: TEAM General Data Framework...7 Figure 2-2: Conceptual Relationship between Economic Activity and Emissions...13 Figure 4-1: Most frequently reported PCS parameters (based on non-zero annual loadings)...33 Figure 4-2. SIC-NAICS crosswalk for assigning direct discharges to 4-digit NAICS sectors Figure 7-1: Conceptual Relationships between Economic Activity and Emissions...91 Abt Associates Inc., September 29, 2009 Contents vi

8 Chapter 1: Introduction 1.1 Objectives Executive Order of November 16, 1999 requires the U.S. Trade Representative, through the interagency Trade Policy Staff Committee (TPSC), to conduct environmental reviews of trade agreements. The Trade Representative has the authority to determine whether an environmental review is warranted based on the significance of foreseeable environmental impacts. The U.S. Environmental Protection Agency, a member of the TPSC, is committed to working with the U.S. Trade Representative and the other members to assess how changes in economic activity resulting from trade agreements may affect domestic environmental quality. Understanding the environmental impacts of structural changes in the U.S. economy requires a comprehensive approach to assessing the economy and its relationship with natural resources. The Trade and Environmental Assessment Model (TEAM) is designed to estimate a wide range of environmental impacts resulting from national economic changes. The comprehensive approach used in TEAM is meant to account for the potential for environmental impacts that may not come only from the sectors directly affected by a proposed change in trade policy but also from changes in indirectly affected sectors. EPA plans to use TEAM as a screening tool in support of the environmental review process to highlight potentially significant impacts that may warrant more detailed analyses. While the model was developed to help highlight the environmental impacts of trade policies, it may also be applied more generally to evaluate the environmental impacts of economic changes resulting from other events. TEAM complements other analytic frameworks previously developed by EPA to assess the environmental effects of specific regulations and other actions affecting individual pollutant media (e.g., water pollutant discharges), or to assess the broader environmental concerns of a particular industry. In developing TEAM, EPA sought to develop an analytic framework that is capable of addressing, in a comprehensive and consistent fashion, the environmental effects of trade agreements or other complex economic events potentially affecting a wide range of industries and environmental media. At the outset, EPA outlined several analytic objectives to be accomplished in TEAM. These objectives reflect the breadth of environmental media, industrial sectors, and geographic locations that might reasonably be expected to be affected by a trade agreement, and include: 1. Ability to assess environmental effects associated with a wide range of environmental media, including the following environmental release and resource use categories: Criteria and Hazardous Air Pollutant 1 emissions from stationary and mobile sources; Water pollutant discharges across a wide range of chemical categories; Carbon dioxide and other greenhouse gas emissions associated with energy consumption; and Energy consumption by fuel type. For these environmental media, EPA sought to understand how environmental releases or resource use would be expected to change as the result of the changes in economic activity from a trade agreement or other economic event. 2. Ability to address environmental effects occurring broadly throughout the economy. Specifically, EPA sought to use the North American Industrial Classification System (NAICS) as the economic framework 1 As defined by the Clean Air Act. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 1

9 for analysis and to be as comprehensive as possible in its coverage of industries within the NAICS sector framework. 3. Ability to capture the geographical distribution of environmental effects, in terms of both the specific locations and quantities of changes in environmental releases from the economic activities affected by a trade agreement. To meet these objectives, EPA sought to use the affected production entity (e.g., manufacturing facility), where possible, or the state, as the basic locational unit of analysis for assessing environmental effects of a trade agreement. Further, to provide the desired understanding of how environmental releases might vary across the country, EPA sought detailed information on the current patterns of economic activity and environmental releases by production entity and state as the baseline information from which the environmental effects of a trade agreement would be analyzed. EPA judged that these levels of analytic resolution and comprehensiveness of analytic coverage in terms of environmental effects, economic sectors, and locations of effect would provide a comprehensive and consistent screening-level assessment of the environmental effects of a trade agreement. 1.2 Overview of the TEAM Analytical Framework To meet these objectives, TEAM applies a framework that derives from one of the fundamental analytic frameworks of economics Input-Output Analysis. Input-output analysis seeks to understand, at a specified level of economic sectoral and regional resolution, the distribution and quantities of economic inputs that are required to produce economic outputs. Consistent with the input-output conceptual framework, TEAM treats environmental releases and resource use as though they were explicit factor inputs in the production of the economic goods and services that may be affected by a trade agreement. Specifically, TEAM is built around emission factors that describe the relationship between the economic value of output, by economic sector and location, and the quantity of environmental releases that occur in conjunction with the production activity. These emission factors are conceptually equivalent to input-output coefficients 2 in the traditional economic input-output framework. To meet the objectives of analytic resolution and coverage outlined above, these emission factors are defined as follows: By specific pollutant/use parameter for each of the three broad environmental release/resource use categories outlined above: air emissions, water pollutant discharges, energy use, and carbon emissions. Overall, TEAM accounts for approximately 1,100 specific chemicals, chemical groups (e.g., metal compound groups), and resource use categories. For each of digit NAICS economic sectors (except for energy use and carbon emissions, for which more aggregated economic sectors are used). For each state where the economic activity occurs (except for certain energy use or carbon emissions categories, which are defined at the national level). TEAM draws on data from many sources and combines them in a consistent framework. TEAM s emission factors are based on environmental release and resource use inventories compiled by EPA, the U.S. Department of Energy, and the U.S. Census Bureau, coupled with economic activity data compiled in the U.S. Economic Census and the U.S. Agricultural Census, and data published by the U.S. Department of 2 Technically, the direct requirements coefficients within standard input-output analysis terminology. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 2

10 Energy. These data underlie TEAM s emission factors and also describe the baseline profile of economic activity and environmental releases/resource use from which TEAM calculates estimated changes in environmental release and resource use. The model brings together these usually separate data characterizing domestic economic activity, and environmental releases and resource uses into a single consistent framework, in terms of time period, economic sector classification, and geography. TEAM is flexible and accepts many forms of inputs to the model. For a TEAM analysis, the economic event (such as economic changes resulting from a trade agreement) is specified as the estimated changes in national economic activity for a given year by economic sector. 3 TEAM may receive the trade agreement/economic event in its native sector classification framework i.e., 4-digit NAICS sector or in several other economic classification frameworks such as the BEA commodity codes, which TEAM converts to the NAICS framework. Additionally, TEAM may receive the changes in economic activity for the primary effect sectors only (e.g., the changes in those economic sectors directly affected by a trade agreement or other economic event) or for total effect sectors (i.e., the changes the primary effect sectors plus changes in other sectors linked economically to the primary effect sectors). When the changes in economic activity are defined in terms of primary effect sectors only, TEAM can translate these changes into total sector effects using information from the most recent U.S. Input-Output benchmark accounts tables. TEAM includes the option to account for total sector changes that occur only in domestic production activities, through the use of import-adjusted input-output total requirement coefficients. It also includes the option to account for the different emissions/resource use profile of certain sector inputs (in particular electricity), based on the geographical distribution of the primary economic impacts using NERC-adjusted total requirement coefficients. 4 To calculate the changes in environmental releases and resource use, TEAM multiplies the changes in economic activity by sector times the emission factors for each sector, location, and environmental release/resource use categories. TEAM presents results that are accessible at different levels of detail. TEAM calculates environmental release/resource use and economic data for a combination of individual facilities and states, but presents the changes and impacts at the geographical level of states. This framework allows the model to draw on and preserve data available at a high level of detail, while also allowing aggregation to higher levels, for example 3 4 At present, the TEAM application does not allow the user to specify an economic event at a level of resolution other than national, however, the TEAM analytical framework could be adapted to consider regional or state variations in anticipated economic impacts. This NERC-adjusted analytic framework enables the user to estimate the changes in water and non-ghg air pollutant and GHG air emissions associated with incremental electricity purchases, accounting for the location of where electricity is consumed (as reflected by baseline economic activity for each electricity-consuming sector) and regional differences in the GHG intensity and intensity of water and non-ghg air pollutants of supplied electricity. The assessment of differential water and non-ghg pollutant and GHG emissions effect is reported in two ways: (1) At the national level for an individual electricity consuming industry i.e., water and non-ghg pollutant and GHG emissions per dollar of national consumer industry output, reflecting the national profile of an industry s production and the specific carbon emissions intensity of the NERC regions in which that production occurs; and (2) By the individual NERC region components of production for an individual electricity consuming industry i.e., the NERC region-specific water and non-ghg pollutant and GHG emissions per dollar of consuming industry output, in a specific NERC region. This latter concept supports understanding the differential burden of water and non- GHG pollutant and GHG emissions within the regional segments of an industry, based on the differences in the GHG water and non-ghg pollutant and GHG emissions profile of electricity production in the regions in which the industry segments are located. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 3

11 by aggregating state-level results to the national level, or by aggregating 4-digit NAICS sectors to 2-digit sectors. TEAM reports the calculated changes in environmental releases and resource use as absolute values of change and percentage changes in relation to the baseline profile of releases and resource use. TEAM can translate the changes in environmental releases into toxicity normalized changes using normalization factors that account for the differential toxicity and human health risk posed by the various chemicals included in the TEAM framework. 1.3 TEAM Development History Formal development of TEAM began in 2001 and continues currently with enhancement to TEAM s analytic and reporting capabilities, updating of baseline datasets, and ongoing review, testing and validation of TEAM s analytic systems. Most recently, the TEAM framework was updated to: (1) incorporate the latest Economic Census data available for 2002 and the corresponding environmental release data; (2) implement adjustments for imported intermediate inputs in the input-output total requirements coefficients; (3) add functionalities to estimate changes in energy and fuel consumption and the resulting carbon dioxide emissions; and (4) implement adjustments to the economic and emissions baseline and to the input-output total requirements coefficients to define the electrical power generation sector at the level of North American Electric Reliability Corporation (NERC) regions. This documentation reflects this most recent version of TEAM. The current version of TEAM differs from the prior version of the model documented in an earlier report (Abt Associates, 2004) 5, in several regards: The prior version of TEAM used a baseline of 1997 for economic and environmental data. The economic baseline was largely based on the 1997 Economic Census and 1997 Census of Agriculture. The current version of TEAM uses a 2002 baseline which corresponds to the most recent Censuses. The prior version of TEAM defined activity and emissions at a more detailed level of geographical resolution (counties) and economic sectoral resolution (6-digit NAICS). Although this finer resolution allowed potentially more detailed analyses, it also created considerable data uncertainty given the level of resolution typically used by the primary data sources. Specifically, a substantial fraction of revenue that would otherwise be reported by the Economic Census at the level of counties and 6-digit NAICS is instead only reported at a higher level of geographic and/or economic sector resolution to avoid disclosure of information for individual respondents. The prior version of TEAM used various disaggregation algorithms to bring these data to the desired level of analytic resolution. Similarly, individual emission values often did not have a known single 6-digit NAICS industry sector but were associated with more general industrial classification codes, which required emissions to be apportioned over a potentially large number of sectors. These disaggregation methods introduced substantial, unquantifiable uncertainty in the baseline datasets underlying the TEAM analysis. The current version of TEAM uses the states and 4-digit NAICS sectors as units of analysis. This resolution is closer to that used in the primary data sources and therefore reduces the uncertainty associated with estimating suppressed values or reassigning emissions across sectors. 5 The previous version of the TEAM framework, which was based on 1997 economic and environmental release data Trade and Environmental Assessment Model: Model Description, prepared by Abt Associates, Inc., April 14, 2004 (EPA contract #68-W , Task Order #25). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 4

12 The prior version of TEAM included different media types. In addition to certain media types also included in the current version of TEAM (direct and indirect water dischargers, and point source, area, and mobile air emissions), the prior version of TEAM also included data on hazardous waste generated, agricultural chemicals (pesticides and fertilizer) applied, land use, and water use. These additional media types were not included in the current version of TEAM because more recent data were not available (to coincide with the 2002 baseline), or they did not provide significant analytical insight into the environmental impacts of trade agreements. However, the current version of TEAM incorporates other media types energy use and carbon emissions that were not included in the earlier version of the model. This documentation pertains only to the current version of TEAM. For more information on the prior version of the model, see Abt Associates (2004). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 5

13 1.4 Document Organization The following sections of this document provide extensive information on the TEAM framework, including its data sources and steps required to assemble the data in a consistent fashion for use in TEAM, and the calculation and interpretation of environmental impacts. The document is divided into nine chapters including this Introduction: Chapter 2 summarizes the overall data and analytical framework used by the model. Chapter 3 discusses the first underlying data component of the TEAM model: the baseline economic activity data. Chapter 4 discusses the second underlying data component of the TEAM model: the baseline emissions data. Chapter 5 describes how TEAM conceptualizes and defines economic changes, and translates these changes into resulting environmental impacts. Chapter 6 describes how TEAM informs the assessment of the significance of the environmental impacts predicted by the model in terms of potential impacts on human health and welfare. Chapter 7 describes the model software interface. Chapter 8 summarizes key assumptions and uncertainties. Chapter 9 provides references to more detailed information about key elements of the model. The document also contains six appendices, which provide additional technical detail on certain topics: Appendix A Development of TEAM Support Files Appendix B Supporting Material for TEAM Economic Baseline Appendix C Supporting Material for TEAM Emissions Baseline Appendix D Energy Consumption and Carbon Emissions Adjustments Appendix E Review of Test Case and Results Appendix F Estimation of Emission Factors Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 6

14 Chapter 2: General Data and Analytical Framework Figure 2-1 illustrates the key components of the TEAM framework. Figure 2-1: TEAM General Data Framework The primary inputs to the model, shown in the upper left on Figure 2-1, are the economic changes being modeled, i.e., the trade or other economic event that is inducing changes in the production levels of specific U.S economic sectors. The model then uses static economic and emissions baseline data sets to calculate emissions factors, and generate the primary output of the model consisting of projected emissions/resource Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 7

15 use changes for each sector and/or geographical location (e.g., state). Two main types of baseline data are used in the model. These are shown as curved horizontal box objects on Figure 2-1: (1) economic output by sector and state, and (2) emissions by state, or by facility. As illustrated in Figure 2-1, TEAM combines the economic and emissions baseline to determine implied empirical emissions factors for each location (i.e., state) and economic sector. It then uses these emission factors to calculate the expected increase or decrease in emissions or resource use. The state-level baseline economic data serve a dual purpose: they are used in calculating state-level emission factors for environmental impact categories having emissions at the state level, and they are also used to determine the percent change in state economic output resulting from the economic event being modeled. TEAM is built around several key framework elements, which we review in the remainder of this section: TEAM uses emission factors and concepts from Input-Output Analysis to understand the relationship between pollutant releases and economic activity; TEAM uses a linear, fixed coefficients production model to estimate changes in pollutant releases in relation to changes in economic activity; TEAM uses the North American Industrial Classification System (NAICS) as the economic framework for organizing data and analysis in TEAM; TEAM generally uses the state as the geographical framework for analysis, preserving, wherever possible, the identity of the individual production entity observed (e.g., point source air emitter); 6 and TEAM uses individual categories of pollutant release and resource use as the framework for understanding primary environmental impacts. 2.1 Using an Input-Output Analysis Framework to Understand the Relationship Between Pollutant Releases and Economic Activity TEAM follows a method that goes back to Ayres and Kneese (1969), Kneese, Ayres and D arge (1970), and Leontief (1970), and which takes the view that pollution emissions are a fundamental part of production processes, just like raw material inputs, and thus can similarly be treated as an input within an economic Input-Output Analysis framework. This early work was an effort to bring economic analysis more in line with the fundamental law of conservation of mass by showing that pollution externalities were intrinsic to economic processes, and not an exceptional case to be addressed through a partial equilibrium analysis of economic welfare (Ayres and Kneese, 1969, p. 283). It is now common practice in environmental economics to treat emissions as an input to production, or as a component of the production function. 7 TEAM builds on the capabilities of the Environmental Input-Output Model previously developed by Abt Associates for the U.S. Environmental Protection Agency (Abt Associates Inc., 2000) to support analysis of greenhouse gas emissions in relation to the structure of the U.S economy. The Environmental Input-Output Model and TEAM both rely on a general economic analysis framework called Input-Output Analysis. 6 7 As described in Section 4.3, certain energy use and carbon emission categories are calculated at the national level rather than the state, reflecting the resolution at which the primary data were available. A formal analysis of externalities is available in Baumol, William J., and Wallace E. Oates (1988). The Theory of Environmental Policy, Second edition, New York: Cambridge University Press. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 8

16 Input-output analysis is used to understand the composition of inputs required to produce the outputs of an economy, and is typically conducted at the level of a national or regional economy. Input-output analysis is conducted within an economic classification framework of input and output sectors (or commodities): the output of each sector (or an output commodity) is analyzed in terms of the required inputs from various sectors (or commodity inputs). 8 The primary input-output relationships i.e., those that indicate the inputs that are required from directly supplying sectors are described in terms of direct requirements coefficients. The direct requirements coefficients for an output sector indicate the dollar value of inputs required from each directly supplying input sector that is required to produce a dollar of output from the output sector. Table 2-1 illustrates input-output relationships for a simplified hypothetical economy composed of three sectors: Agriculture, Manufacturing, and Services. The columns of the table represent the economy s output sectors and the rows of the table represent the economy s input sectors. Each column reports, for each column output sector, the inputs by row input sector that are directly required to produce the output of the column sector. For example (in light blue in Table 2-1), the Agriculture sector requires 3 dollars of inputs from other firms within the Agriculture sector itself, 2 dollars of inputs from the Manufacturing sector, and 1 dollar of input from the Services sector, to produce a total output of 6 dollars. Each input row reports the destination of the output of a sector as inputs to the production of the column outputs. For example (in light green in Table 2-1), the Manufacturing sector supplies 2 dollars of inputs to the Agriculture sector, 3 dollars of inputs to itself (the Manufacturing sector), and 1 dollar of input to the Services sector. The values shown in parenthesis are these direct input values expressed per dollar of output, and are the direct requirement coefficients for each output sector i.e., the value of input required from each input sector to produce a dollar of output from each output sector in this hypothetical economy. For example, to produce a dollar of agricultural output, the Agriculture sector requires 50 cents of input from the agricultural sector, 33 cents of input from the Manufacturing sector, and 17 cents of input from the Services sector. The last column of the table shows that the output (price times quantity) of each sector is equal to 6 dollars, and the total gross domestic product of this economy is 18 dollars. The last row of the table shows that the total value of inputs required to produce the output of each column sector, 6 dollars, and again shows that the total gross domestic product of this economy is 18 dollars. Table 2-1: Input-Output Relationships for Three Sectors in a Hypothetical Economy Output Sectors Total Input Sectors Agriculture Manufacturing Services Outputs Agriculture 3 (0.50) 1 (0.17) 2 (0.33) 6 Manufacturing 2 (0.33) 3 (0.50) 1 (0.17) 6 Services 1 (0.17) 2 (0.33) 3 (0.50) 6 Total Inputs 6 (1.00) 6 (1.00) 6 (1.00) GDP = 18 Beyond the understanding of these primary input-output relationships, input-output analysis provides the very important ability to understand the total requirements of inputs for producing the output of a given sector, including not only the inputs from the directly supplying sectors (as illustrated in the above example), 8 Input-output analysis may be conducted in terms of economic sectors which typically produce a relatively, but not perfectly, homogeneous set of outputs, i.e., commodities or in terms of the commodities that are produced or required as inputs for production, regardless of the specific sector that produces or supplies the commodities. The following discussion uses the term, sector, to describe input-output analysis; however, the same descriptions and understandings of input-output analysis concepts apply to a commodity-based input-output framework. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 9

17 but from all the sectors that supply to the directly supplying sectors, and in turn to those input sectors, and so on, ad infinitum. This concept is achieved via mathematical manipulation of the direct requirements coefficients and produces a matrix of total requirements coefficients (also referred to as the Leontief Inverse Matrix in reference to Wassily Leontief s seminal work on the input-output analysis framework (1951, 1953) 9 ), which describe the dollar value of inputs from both directly and indirectly supplying sectors that is required to supply a dollar of final demand from each output sector. The concept of final demand restricts the output from a sector to that which is available for final consumption in the economy and thus excludes the outputs from a sector that, in turn, may be needed as inputs from itself directly or indirectly to achieve a dollar of output delivery to final demand. The input-output framework is a powerful tool for describing the relationships between economic sectors at a given point in time. Its strengths lie primarily in the detail and richness of view it provides of an entire economy, with only modest computing requirements. It also makes use of readily available data that have a proven track record of reliability, within a well-accepted and transparent methodology that has been fully developed and documented in the literature. The Bureau of Economic Analysis (BEA), which produces a set of national input-output benchmark accounts for the United States economy, uses the input-output framework to study industry production, or to prepare other economic statistics. For example: 10 To estimate the direct and indirect effects of changes in final uses on industries and commodities; for example, to estimate the effects of a strike or a natural disaster on the economy, or, supplemented with additional information, to estimate the effects of an increase in U.S. exports on employment. To provide detail that is essential in determining weights for price indexes, such as the producer price index that is compiled by the Bureau of Labor Statistics, and quantity indexes, such as the quantity index for gross domestic product by industry compiled by the Department of Commerce s Industry Economics Division (IED). To provide the basis for benchmarking the National Income and Product Accounts every 5 years. To provide a framework and data for the preparation of other economic statistics, such as the transportation satellite accounts and the travel and tourism satellite accounts, both of which are prepared by IED. Beginning in the 1960s, input-output analysis was extended to capture the linkages between the economy and the environment. The concept is now well established. 11 In this application of input-output analysis, pollutant emissions are treated as factor inputs to the production of economic outputs, as conventionally defined and measured in input-output analysis. Using the standard terminology of input-output analysis, the direct requirements coefficients describe the quantity of pollutant emissions directly required to produce the output of an economic sector. In this context, these direct requirements coefficients are defined as the physical quantity of directly produced pollutant emissions per dollar of output. The concepts of input-output analysis may also be extended to understand the total requirements of pollutant emissions to deliver a dollar of output For more on Wassily Leontief (Winner of 1973 Nobel Prize in Economics) and the theory behind the input-output coefficients, see also: "Wassily Leontief and His Contributions to Economic Accounting" Survey of Current Business, March 1999, and Leontief, Wassily W., Input-Output Economics. 2nd ed., New York: Oxford University Press, Examples provided on BEA s website at For reviews of environmental input-output models, see Duchin and Steenge (1999), and Miller and Blair (1985). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 10

18 to final demand. The interpretation of these pollutant emissions total requirements coefficients is exactly analogous to that for the traditional economic input-output analysis: these total requirements coefficients are defined as the physical quantity of directly and indirectly produced pollutant emissions per dollar of output to final demand. The TEAM framework relies on concepts from Input-Output Analysis in two ways: 1. TEAM uses the concept of the direct requirements coefficient as the basis for its so-called emission coefficients or emission factors. TEAM s emission factors differ from the more common concept of emission factor as used in environmental engineering analyses by being defined in relation to the economic value of output quantity of pollutant emissions or resource use per dollar value of output instead of in relation to a physical unit of operation or production. As discussed in the following section, these emission factors are the core concept for estimating the environmental impacts of changes in economic activity, as analyzed in TEAM. 2. TEAM uses the concept of total requirements analysis to understand the total economic and total emissions/resource use effects of changes in production levels in primary impact sectors. As described more fully in Section 7.2: Scalability of Environmental Impacts, TEAM can 12 use a total requirements matrix derived from the U.S. Input-Output Accounts to convert a set of production changes in primary impact sectors into changes throughout all sectors and thus account for both the direct and indirect economic sector linkages to the primary impact sectors. In this case, the emissions/resource use effects estimated by TEAM become total sector effects i.e., reflecting the production changes in both primary effect and indirectly affected sectors. As with any economic model, the Input-Output framework makes certain simplifying assumptions, which are inherited by TEAM. One particularly strong assumption concerns the use of fixed coefficient production functions. Production in an Input-Output model is homogeneous of degree 1, meaning that increasing output by a factor N requires all the inputs to be increased by that same factor. The marginal product of every input is equal to the average product, and there are no possibilities for factor substitution, i.e., the technology is assumed fixed. This is not a significant difficulty when considering small changes in output and short time horizons, but as impacts grow larger and the time to adjust gets longer, these assumptions pose real limitations on the predictive capacity of the model. Another assumption of the input-output analysis framework is that there are no constraints on inputs or outputs. The model assumes that inputs are supplied without limitations and at a constant price, and that all outputs can be sold at a constant price. Again, these assumptions are reasonable for small changes and short time horizons, which are expected to be the type of scenarios modeled in TEAM. Alternative approaches exist to develop industry-specific emissions factors linking economic outputs to pollutant emissions and resource use. One approach relies on the evaluation of emissions from a representative set of technologies and building engineering-based estimates of emission patterns within an industry. These bottom up estimates, however, are subject to the difficulty of defining a representative 12 Whether the conversion to primary economic impacts into total impacts is needed or appropriate depends on the conceptual definition of the economic event being analyzed by TEAM. If the event is defined in terms of impacts in primary sectors only, then it may be appropriate to estimate the total impacts across all sectors resulting from the changes in the primary impact sectors. However, if the economic event is already defined in terms of total sector impacts, then a second conversion to total sector economic impacts would be meaningless. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 11

19 facility, and linking the parameters from which these technology-based emissions are developed (e.g., fuel consumption) to production quantities and economic values. A second, top down approach relies on the use of data on actual observed emissions, if such data exist, and economic production levels, to estimate emission factors for a given industry and location framework. This empirical method for developing emission factors, which builds on existing national emissions and resource use inventories, is used in TEAM. As discussed in later sections, this approach may be applied over a range of geographical and economic sector levels of resolution. As applied to the development of TEAM s emission factors, this empirical approach immediately addresses the issues relative to application of the engineering bottom up approach noted above. In particular, the empirical emission factor estimates implicitly reflect the blend of production technologies and specific structure of inputs in relation to output for the given facility or other locational and economic framework being analyzed. In addition, because the empirical method directly relates emissions to the value of economic activity, this method also avoids having to link an engineering-based emission factor, which relates emissions to a physical activity level, to the economic value achieved by that physical activity. 2.2 Using a Linear, Fixed Coefficients Production Model to Estimate Changes in Pollutant Releases in Relation To Changes in Economic Activity The empirical emission factor framework, as outlined above, provides insight into the relationship at a point in time between pollutant releases/resource use and the economic value of production achieved in generating the pollutant releases/resource use. To estimate the changes in releases/resource use that result from changes in production levels, TEAM applies these emission factors within the framework of a linear, fixed coefficients production model. That is, as the production level in a given sector and state changes as a result of the trade or other economic event analyzed by TEAM, pollutant releases and resource use change in a direct and linear way with the change in production level. In the field of economics, this type of relationship between production outputs and inputs is referred to as a linear, fixed coefficients production model. 13 Figure 2-2 illustrates this conceptual framework. As described above, the emission factor is defined as the baseline emissions/resource use associated with a sector and geographical location, divided by the value of shipments or revenue for that same sector and location. TEAM then calculates changes in emissions and resource use by multiplying the change in economic activity in a given NAICS sector and location by the emission factor associated with this sector and location. The constant relationship, within a state and sector, between economic activity and emissions, is represented by the slope of the function illustrated schematically in Figure 2-2, where emissions (on the vertical axis) are a function of the production level as quantified by the value of shipments (on the horizontal axis). 13 In this context, linear means that the relationship between inputs and outputs is directly scalable and fixed coefficients means that the relationships between inputs and outputs (for TEAM, the emission factors) are fixed. An alternative economic characterization of this type of input-output relationship is that emissions and production are linked in a constant elasticity framework, with an elasticity value of one. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 12

20 Figure 2-2: Conceptual Relationship between Economic Activity and Emissions. 2.3 Using the North American Industrial Classification System (NAICS) as the Economic Framework for Organizing Data and Analysis in TEAM TEAM classifies economic activities, and the value of production, emissions and resource use to which they are associated, in terms of the industry sector in which they occur. TEAM uses the standard North American Industry Classification System (NAICS) at the 4-digit level to classify both the economic and emissions/resource use data components. NAICS was developed jointly by the U.S., Canada, and Mexico to provide comparability in statistics about business activity across North America. NAICS classifications have progressively been replacing the previous U.S. Standard Industry Classification (SIC) system in all national economic statistics. The NAICS sector classification system presents itself as a tree composed of the 19 main 2-digit economic sectors and sector groups listed in Table 2-2. Table 2-2: NAICS 2-digit sectors NAICS Code Sector Description 11 Agriculture 21 Mining 22 Utilities 23 Construction Manufacturing 42 Wholesale trade Retail trade Transportation & warehousing 51 Information 52 Finance & insurance 53 Real estate & rental & leasing 54 Professional, scientific, & technical services 55 Management of companies & enterprises 56 Administrative & support & waste management & remediation services 61 Educational services 62 Health care & social assistance 71 Arts, entertainment, & recreation 72 Accommodation & foodservices 81 Other services (except public administration) Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 13

21 These 2-digit sectors subdivide into progressively more detailed subsectors represented by 3-, 4-, 5- and 6- digit numeric codes. Table 2-3 provides an extract of the NAICS family tree for the chemical manufacturing sector, which is included within the manufacturing sector group (31-33). As the example from the table shows, the resolution of economic activity can be very general (NAICS 325: chemical manufacturing), slightly more detailed (NAICS 3251: basic chemical manufacturing), specific (NAICS 32513: dye and pigment manufacturing), or very specific (NAICS : synthetic dye and pigment manufacturing). At each level, the NAICS sector code gains one more digit. In concept, each of the 2- to 5- digit levels are fully defined by the total of their respective subsectors, i.e. a 4-digit sector is composed of the total of its associated 5-digit subsectors. TEAM is designed to generally store and process data at the 4-digit NAICS economic sector level (highlighted in light blue in the example of Table 2-2). 14 Because of the structure of the NAICS framework, however, it is always possible to determine, from the 4-digit level, the composition at all upper levels of the structure (2- and 3-digit levels). Table 2-3: NAICS framework, as implemented in the 2002 Economic Census NAICS Code Description Number of Establishments Value of Shipments ($1,000) Number of Paid Employees Manufacturing 350,828 3,916,136,712 14,699, Petroleum & coal products manufacturing 2, ,312, , Chemical manufacturing 13, ,424, , Basic chemical manufacturing 13, ,424, , Petrochemical manufacturing 2, ,710, , Industrial gas manufacturing 56 21,084,070 9, Dye & pigment manufacturing 572 5,864,978 10, Inorganic dye & pigment manufacturing 204 6,338,477 14, Synthetic organic dye & pigment manufacturing 81 3,522,308 7, Other basic inorganic chemical manufacturing 123 2,816,169 7, Alkalies & chlorine manufacturing ,927,017 55, Carbon black manufacturing 41 2,809,496 6, All other basic inorganic chemical manufacturing 25 1,033,515 1, Other basic organic chemical manufacturing ,084,006 47, Resin, synthetic rubber, & artificial & synthetic fibers & ,223, ,244 filaments manufacturing Resin & synthetic rubber manufacturing ,273,901 76, Plastics and Rubber Products Manufacturing 15, ,369, , Exceptions to the use of the 4-digit NAICS framework include the energy use and carbon emissions categories which are defined at the level of 3-digit NAICS or for highly aggregated industry sectors, as described in Section 4.3. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 14

22 2.4 Using a Geographical Framework Based on the State or Observed Production Entity TEAM is designed to perform calculations using states as the default unit of analysis. In the case of certain energy use and carbon emissions media types for which source data were not available at the state level, TEAM provides national-level results (see Section 4.3). As will be discussed in Sections Chapter 3 and Chapter 4, the use of facilities (when available for emissions data), states, or the United States as units of analysis allows TEAM to preserve the level of detail reported by the original sources of data used as economic and emissions/resource use inputs to the model. 2.5 Using Individual Categories of Pollutant Release and Resource Use as the Framework for Understanding Primary Environmental Impacts TEAM aims to capture the range of potential environmental impacts occurring as a result of economic changes such as those associated with a trade event. The model captures impacts associated with the release of pollutants in air and water, and with energy use. As discussed in Section 2.1 above, the modeling framework calculates emissions/resource use factors by using inventories of actual emissions and resource use. The model currently captures the impacts of: Air emissions (point, area and mobile sources) of toxic and criteria air pollutants Water discharges (direct and indirect) Energy use, by fuel type Carbon dioxide (CO 2 ) emissions associated with energy use (fuel and non-fuel) Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 15

23 Chapter 3: Baseline Economic Activity Data As described in Chapter 2, TEAM combines economic data with emissions and resource use data to calculate the implicit relationship between emissions and economic production the emission/resource use factors and to estimate changes in emissions and resource use that would result from economic changes. The model is designed to generally evaluate the environmental impacts of economic changes at the geographic resolution of states, and at the economic resolution of 4-digit NAICS sectors. 15 The TEAM model includes baseline economic activity data at the national and state levels. During TEAM execution, the national-level economic baseline is used to calculate the economic changes, in percentage terms, expected to result from the modeled scenario. The state-level baseline economic data are used with the emissions/resource use baseline data to calculate state-level emission factors, and thus projected emissions, for economic activities. The state-level baseline economic data were also used during the development of the emissions/resource use baseline data, as will be described in Chapter 4. This section describes the compilation of the economic baseline data at the 4-digit NAICS level from two primary data sources: the 2002 Economic Census (Section 3.1) and the 2002 Census of Agriculture (Section 3.2). Each section discusses the compilation of data at the national and state levels of spatial resolution. 3.1 Economic Census Data The primary source of TEAM baseline economic data is the U.S. Economic Census, the major economic statistics program of the United States. The Department of Commerce s Census Bureau performs a Census survey of all establishments every 5 years, under authority granted by Title 13, U.S. Code. Title 13 makes mandatory the response by business firms or individuals queried by the census, establishes penalties for noncompliance, and requires that the Census Bureau maintain the confidentiality of the information provided by respondents. TEAM uses the most recent census data publicly available, which is the 2002 Economic Census. The 2002 Census collected information from 7 million companies representing business establishments in 1,070 industry classifications. The Census Bureau also compiled data on 17 million businesses without paid employees. Overall, the 2002 Census covers approximately 97 percent of U.S. business receipts. As shown in Table 3-1, the Agriculture, Forestry, Fishing, and Hunting sector (NAICS 11), the Public Administration sector (NAICS 92), and a few other activities (subsectors within NAICS 48-49, 52, 61, and 81) are not included within the 2002 Economic Census. Certain components excluded from the Economic Census in particular the crop production and animal production industry sectors are found in the 2002 Census of Agriculture, which is discussed separately in Section Exceptions to this approach include the carbon emissions and energy use media types which TEAM treats at more aggregated levels of geographical and economic resolution (i.e., country and/or 3-digit NAICS or aggregated industry sectors). For more information on carbon emissions and energy use data, see Section 4.3. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 16

24 Table 3-1. Sectors Outside the Scope of the 2002 Economic Census 2-Digit NAICS Sector 11: Agriculture, Forestry, Fishing and Hunting Excluded Components 48-49: Transportation and Warehousing 4821: Rail Transportation 4911: Postal Service 111: Crop Production* 112: Animal Production* 113: Forestry and Logging 114: Fishing, Hunting and Trapping 115: Support Activities for Agriculture and Forestry 52: Finance and Insurance 5251: Insurance and Employee Benefit Funds 61: Educational Services 6111: Elementary and Secondary Schools 6112: Junior Colleges 6113: Colleges, Universities, and Professional Schools 81: Other Services (Except Public Administration) 8131: Religious Organizations 8141: Private Households 92: Public Administration All sectors (NAICS ) * NAICS sectors 111 and 112 are found in the U.S. Census of Agriculture, described in Section 3.2. The U.S. Census Bureau collects information for each establishment, defined as a physical site, plant, store, or other business location, i.e., the actual location of the economic activity. A single company may thus be associated with multiple establishments. Establishment data include the kind-of-business activity, physical location, form of ownership, dollar volume of business in 2002, number of employees, and dollar amount of payroll. In addition to these core items, the Census questionnaires collect industry-specific questions, e.g., inputs, costs, product lines. The Economic Census reports data according to NAICS, with each establishment assigned a numerical code corresponding to its industrial classification under the NAICS structure. The Economic Census dataset summarizes key statistics by geographical unit (e.g., nation, state, county), at the various levels of details of the NAICS structure, for example: NAICS Description 33 Manufacturing 331 Primary Metal Manufacturing 3312 Steel Product Manufacturing from Purchased Steel Rolling and Drawing of Purchased Steel Steel Wire Drawing This arrangement is referred to here as the NAICS ladder. As one goes down the ladder to more specific NAICS codes, fewer establishments are present. If only a few firms are present for a given rung on the NAICS ladder, data are not disclosed publicly to retain confidentiality of individual responses. Furthermore, Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 17

25 non-disclosure may affect reporting at the higher rungs on the NAICS ladder to prevent simple inference from revealing the information otherwise sought to be protected at a lower reporting level. 16 We developed the TEAM baseline economic dataset using the Economic Census at the state level. Key fields contained in the electronic dataset obtained from the Department of Commerce include: State Federal Information Processing Standards (FIPS) code NAICS industry sector code Number of establishments Value of sales, receipts, revenue, shipment, or business done Dollar amount of payroll, and Number of employees As an example, Table 3-2 provides an excerpt of Economic Census data for Massachusetts. The flag D in the value of sales and annual payroll fields indicate records for which there are too few establishments to allow reporting of the information collected. In that case, the number of employees is typically indicated as a range rather than a discrete value. To develop the TEAM economic baseline, we supplemented the Economic Census with additional sources for sectors where the Economic Census reports no data (e.g., the agriculture sector discussed in Section 3.2), where the Economic Census does not report economic data at the state level for the sector, or where data are suppressed at the state level for confidentiality reasons. We also processed the Economic Census data to fill in apparent inconsistencies in the way the data are reported, for instance in cases where data are unavailable for certain 4-digit NAICS sectors, but are available at a higher level of aggregation. The Economic Census provides data at relatively detailed geographical or economic sector resolution (e.g., county and 6-digit NAICS). As the level of resolution increases from the state to the county or from 4-digit to 6-digit NAICS economic sectors, however, so do the frequencies at which data are suppressed to prevent disclosing information regarding individual businesses, and consequently so would the need to estimate suppressed values to ensure completeness of the TEAM economic baseline data set. EPA determined that using data developed at the level of states and 4-digit NAICS economic sectors provides the appropriate level of resolution for analyses envisioned with TEAM while limiting the uncertainty associated with the estimation of revenue suppressed by the Census Bureau For example, if, within a 5-digit NAICS sector, only two 6-digit NAICS sectors have establishments, disclosing data for the 5-digit NAICS sector and one of the 6-digit NAICS sectors will reveal data by simple subtraction about the other 6-digit NAICS sector, even if the intent is to protect data in that 6-digit NAICS sector. In this case, protection of data in the 6-digit NAICS sector requires non-disclosure of data at the 5-digit NAICS sector. The previous version of TEAM, which was developed based on the 1997 Economic Census, used counties and 6- digit NAICS as the unit of analysis. The baseline data, however, required the estimation of a large fraction of the county/6-digit NAICS revenues (and emissions), increasing the underlying uncertainty in the model estimates. The current baseline data, which is developed at the level of states/4-digit NAICS resolves much of this uncertainty since data are generally reported at that level of resolution in the original data sources. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 18

26 Table 3-2: Excerpt from 2002 Economic Census Economy-Wide Key Statistics State Data Set State 2002 NAICS Code Meaning of 2002 NAICS code Number of establish. Sls, shps, rcpts, rev ($1,000) Annual payroll ($1,000) Number of employees Computer & electronic product mfg ,273,090 4,293,801 15, Computer and peripheral equipment mfg Computer & peripheral equipment mfg 97 2,925, ,815 5, Electronic computer mfg 19 D D ( ) Computer storage device mfg 13 D D ( ) Other computer peripheral equipment mfg , ,753 2, Communications equipment mfg 99 4,775, ,236 12, Telephone apparatus mfg 32 2,837, ,173 5, Telephone apparatus mfg 32 2,837, ,173 5, Radio & TV broadcasting & wireless communications equipment mfg 46 1,052, ,385 4, Radio & TV broadcasting & wireless communications equipment mfg 46 1,052, ,385 4, Other communications equipment mfg , ,678 2, Other communications equipment mfg , ,678 2, Audio & video equipment mfg ,233 37, Audio & video equipment mfg ,233 37, Audio & video equipment mfg ,233 37, Semiconductor & other electronic component mfg 301 6,374, ,002 21, Semiconductor & other electronic component mfg 301 6,374, ,002 21, Electron tube mfg 6 46,662 27, Bare printed circuit board mfg , ,302 2, Semiconductor & related device mfg 68 4,322, ,037 7, Electronic capacitor mfg 8 64,036 17, Electronic resistor mfg 5 21,414 9, Electronic coil, transformer, & other inductor mfg 15 25,415 7, Electronic connector mfg 18 97,735 29, Printed circuit assembly (electronic assembly) mfg , ,805 3, Other electronic component mfg , ,982 3, National-Level Economic Data As described above, TEAM uses national economic baseline data at run-time to derive the relative change in economic activity represented by a specified analysis scenario. The national data are also used during the development of the TEAM baseline datasets to estimate, when necessary, values that are suppressed at the state level (see Section 3.1.2). The 2002 Economic Census provides total revenue data at the national level for almost all 4-digit NAICS economic sectors that fall within the scope of the Census. When reported, these values are used directly in the TEAM baseline dataset. Cases where Economic Census revenue data are suppressed or missing and had to be estimated for use in TEAM are described below, along with the methodology used to estimate missing revenue values based on other data available. Overall, of the digit NAICS economic sectors in the national dataset, only eight entries had to be estimated as described below (revenue for NAICS 5232, 5239, 2371, 2372, 2379, 2381, 2383, and 2389). The remaining 285 data points (97 percent of the sectors) are comprised of values either reported directly in the Economic Census or obtained by simply summing across relevant 6-digit NAICS sectors. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 19

27 Revenue Reported at 6-digit NAICS Level: Manufacturing and Mining Sectors Not all industry sectors have data reported at the level of 4-digit NAICS in the Economic Census Geographical Area Series (GAS) tables, even when data are provided at the more detailed 5-digit NAICS and/or 6-digit NAICS levels of aggregation. In particular, the manufacturing sectors (31-33) and mining sectors (21) have values provided at the 5-digit NAICS and/or 6-digit NAICS levels, but not at the 4-digit NAICS level. The data provided for 6-digit manufacturing sectors appear to be complete, i.e., records exist for all 6-digit economic sectors covered by the Census, and none of the 6-digit economic sectors have suppressed revenue at the national level. The number of establishments and revenue for each 4-digit NAICS manufacturing sector were therefore obtained by summing across values reported at the 6-digit level within sectors Revenue for 4-digit NAICS mining sectors were calculated similarly by summing revenue reported at the 6- digit level within the mining sectors Suppressed Revenue: Securities and Commodity Exchanges and Other Financial Investment Activities The 2002 Economic Census suppresses national revenue for 4-digit NAICS 5239 and 5232 to prevent disclosure of individual business information. Revenue, however, are provided for the parent 3-digit sector (NAICS 523: Securities intermediation & related activities) and for the only other related 4-digit sector (NAICS 5231: Securities & commodity contracts intermediation & brokerage). Additionally, data contained in the Economic Census Industry Series (IS) include total revenue for NAICS 523 and for each of the relevant 4-digit sectors. We disaggregated the residual revenue reported in the GAS for sector 532 among sectors 5232 and 5239 by using information from the Industry Series table as follows: 18 Revenue Revenue GAS,5232 GAS,5239 = Revenue = Revenue GAS,523 GAS,523 Revenue Revenue GAS,5231 GAS,5231 RevenueIS, 5232 Revenue + Revenue IS, 5232 IS, 5232 IS, 5239 RevenueIS, 5239 Revenue + Revenue IS, Alternative Concept of Revenue: Construction Sectors The national revenue data for the construction sectors are not directly reported in the Economic Census at the 4-digit NAICS level (all sectors derived from NAICS 23: Construction). To develop national-level estimates, we combined revenue data from lower resolution NAICS sectors (6-digit NAICS) and state-level figures as follows. 18 Although IS data are superseded by later data sets published by the U.S. Census Bureau, we assume that these IS data still provide appropriate relative weights between sectors within the same industry category and therefore can be used to disaggregate the revenue data reported in the GAS. The alternative would have been to use the number of establishments or number of employees reported in GAS as weights, but this would assume that establishments involved in Securities and Commodity Exchanges (5232), Financial Investment Activities (5239) and Securities and Commodity Contracts Intermediation and Brokerage (5231) are all the same size in terms of revenue per establishment, or revenue by employee. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 20

28 NAICS 2361 and 2362 were estimated by aggregating national-level revenue values reported for 6- digit NAICS sectors. NAICS 2373 and 2382 (state-level data are reported for all states) were estimated by aggregating state-level revenue values reported for these sectors to the national level. NAICS 2371, 2372, 2379, 2381, 2383, 2389 (state-level data are not reported for all states) were estimated by first estimating state-level revenue for 4-digit NAICS sectors and then summing across all states to estimate the national revenue. 19 We first disaggregated the residual revenue reported for NAICS 23 across states with suppressed values for NAICS 237 and 238 in proportion to the number of employees reported for each sector in each state. We then used a similar approach to disaggregate revenue for sectors 237 and 238 across relevant NAICS4 sectors in each state. At the NAICS4 level, revenue data are suppressed for up to five states: DC, RI, SC, SD, and WY. We used the number of employees wherenever these data were available, and the number of establishments when the number of employees were suppressed. Specifically, we used the number of establishments to disaggregate residual revenue for NAICS 237 to NAICS 2371, 2372, and 2379 and the number of employees to disaggregate the residual revenue for NAICS 238 to NAICS 2381, 2383, and We then summed the state revenue values to obtain the national revenue by NAICS4 for the six sectors State Data The TEAM analytical framework begins an analysis from economic changes that are specified at the national level by NAICS sector. These national level economic changes are then assumed to relay down to the state in proportion to the baseline levels of economic activity by state (or, potentially, following other regional distributions) in each sector. State economic baseline data are also used in TEAM during the development of the environmental baseline data set to ensure alignment of emissions with economic activity. For some state and NAICS sector combinations, no numeric revenue value is provided. Instead, a flag indicates the reason for data suppression. Table 3-3 describes the various revenue flags and indicates the data that may be provided. Table 3-3. Revenue flags from economic census geographical area series tables Flag Description Data that may be provided D Q Withheld to avoid disclosing data for individual companies; data are included in higher level totals Revenue not collected at this level of detail for multi-establishment firms Number of establishments. Number of employees is provided as a range rather than as a single value. Number of establishments, payroll and number of employees. N Not available or not comparable Number of establishments may be provided Suppressed revenue data at the 3-digit NAICS level represent approximately 0.2 percent of the revenue in dollars reported for the construction sector overall (2-digit NAICS = 23), while suppressed revenue data at the 4-digit NAICS level represent approximately 0.4 percent of the construction sector s overall revenue in dollars. We used the number of establishments for the following sector/state combinations: NAICS 2371 (DC, RI, SC), NAICS 2372 (DC, RI) and NAICS 2379 (DC, RI, SC, WY). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 21

29 Depending on information available in the Census tables, the following approaches were used to populate the TEAM economic baseline revenue values: Record exists and numeric revenue value is provided. We used the reported value. Record does not exist. If no data are reported for a given NAICS sector in a state (i.e., no establishments are reported in the state for the sector), we assumed that no economic activity occurs in the state for that sector. This is different from cases where the database provides a non-zero number of establishments for a sector but where revenue data are suppressed (see below). Record exists, but numeric revenue value has been suppressed (revenue flag is D ). Depending on the data available within the same NAICS ladder or at the national level, we estimated the missing revenue values as follows: If a state s revenue value is missing at the level of 4-digit NAICS but available at the 3-digit level, the 4-digit NAICS sector s national relative share of the 3-digit NAICS revenue is used to apportion the residual 3-digit NAICS revenue for the state (once revenue for any related 4-digit NAICS sectors have been subtracted) to 4-digit NAICS sectors with suppressed values. Revenue NAICS4,National Revenue NAICS4,State = ( Revenue NAICS3,State Revenue NAICS4,State ) Revenue NAICS3,National If a state s revenue value is missing at both the 4-digit and 3-digit NAICS levels but available at the 2-digit NAICS level, the residual 2-digit NAICS revenue for the state is apportioned to the desired 4-digit NAICS sector in proportion to the national relative revenue share for the sector. Revenue NAICS4,National Revenue NAICS4,State = ( Revenue NAICS4,State ) NAICS2,State Revenue Revenue NAICS2,National In addition to the general approaches outlined above, we used sector-specific approaches to estimate statelevel revenue for specific circumstances of missing data in the construction and utilities sectors, as described in the following sections Construction Sector (NAICS 23) The state revenue data for the construction sectors at the 4-digit NAICS level (sectors derived from NAICS 23 Construction ) are suppressed for certain states in the Economic Census GAS data set. Specifically, revenue data are suppressed for NAICS 2371, 2372, 2379, 2381, 2383, and 2389 for up to five states (DC, RI, SC, SD, and WY). The following steps are used to generate the state-level revenue data for all states: Step 1. We disaggregated the residual revenue reported for NAICS 23 across states with suppressed values for NAICS 237 and 238 in proportion to the number of employees reported for each sector in each state. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 22

30 Step 2. A similar approach was then used to further disaggregate residual revenue for sectors 237 and 238 across relevant 4-digit NAICS. In particular, we used the number of establishments to disaggregate revenue for NAICS 2371, 2372, and 2379 and number of employees to disaggregate revenue for NAICS 2381, 2383, and We used the number of employees where the data were available as a better measure of economic activity in a state than the number of establishments. When the number of employees was suppressed at the state level, the number of establishments was used Utilities (NAICS 2211, 2212, and 2213) The 2002 Economic Census suppresses all state-level data for the utility sector (NAICS 21). The Economic Census simply provides a flag Q in place of revenue for these sectors to indicate revenue not collected at this level of detail for multi-establishment firms. The 2002 Economic Census does, however, report utility data at the national level for the three 4-digit NAICS utility sectors (electric, natural gas, and water, sewage and other utilities), as indicated in Table 3-4. We developed state-level data for these sectors by disaggregating available national revenue data. Table Economic Census data for Utility Sector (221). 21 NAICS Sector Description Revenue (thousands) 221 Utilities $398,907, Electric sector $325,028, Electric power generation $79,431, Electric transmission and distribution $245,596, Natural gas distribution $66,515, Water, sewage, and other $7,363, Water supply $5,886, Sewage treatment $831, Steam and air conditioning $645,268 While payroll values and number of employees are provided for some states, they are missing for many states and therefore cannot be used to apportion national revenue, as was done for the construction sector. Furthermore, using payroll, number of employees, or number of establishments to disaggregate national revenue would incorrectly imply similarities in the scale of operations between utility establishments (or by employee). Given the importance of emissions/discharges from the utility sector, TEAM revenue was instead estimated using indicators of the physical activity that generates emissions/discharges in each of the three utility sectors, as follows: Electric sector (NAICS 2211): We used the retail value of electricity generated to apportion national revenue to states (based on 2002 Energy Information Administration (EIA) data on electricity generation and in-state retail price). Natural gas distribution sector (NAICS 2212): We used the retail value of natural gas distributed to apportion revenues to states (based on 2002 EIA data on the volume and price of natural gas distributed to different categories of customers). 21 Additional data are also provided at 6-digit level NAICS resolution. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 23

31 Water, sewage and other systems sector (NAICS 2213): We used the volume of water used for public supply to apportion revenue to states (based on 2000 U.S. Geological Service (USGS) data on public supply water use). Electric Sector (NAICS 2211) NAICS sector 2211 includes the following activities/sub-sectors: 22111: Electric Power Generation: This industry comprises establishments primarily engaged in operating electric power generation facilities. These facilities convert other forms of energy, such as water power (i.e., hydroelectric), fossil fuels, nuclear power, and solar power, into electrical energy. The establishments in this industry produce electric energy and provide electricity to transmission systems or to electric power distribution systems : Electric Power Transmission, Control, and Distribution: This industry comprises establishments primarily engaged in operating electric power transmission systems, controlling (i.e., regulating voltage) the transmission of electricity, and/or distributing electricity. The transmission system includes lines and transformer stations. These establishments arrange, facilitate, or coordinate the transmission of electricity from the generating source to the distribution centers, other electric utilities, or final consumers. The distribution system consists of lines, poles, meters, and wiring that deliver the electricity to final consumers. 22 The national revenue reported for NAICS 2211 was apportioned to the states on the basis of the states relative share of the retail value of net generation. 23 Electricity generation and retail price data published by the EIA 24 were used to calculate a retail value for electricity generated in each state Over three-quarters of Census reported revenue in Sector 2211 are reported in this sub-sector. Other apportionment methods were considered, including: (1) Based on state revenue reported in the 1997 Economic Census, since the 1997 Economic Census reported revenue for NAICS 2211 for all but 13 states; (2) Based on 2002 state value-added data for utility sector as reported by the Bureau of Economic Analysis; (3) Based on 2002 Revenue from Retail Sales of Electricity to Ultimate Customers by Provider and State, as reported by the Energy Information Administration Electricity. The retail value of net generation was determined to provide the most appropriate measure of the relative measure of physical activity that generates emissions captured in TEAM, i.e., electrical power generation, while also accounting for possible electricity pricing differences between states. Aligning the measure of economic activity with electricity generation rather than consumption is expected to be particularly important for states that are substantial net importers of electricity (where emissions would otherwise be lower than suggested by revenue from consumption or retail sales) or are substantial net exporters of electricity (where emissions would otherwise be higher than suggested by revenue from consumption or retail sales). For example, the District of Columbia (DC) consumes five times more electricity than it generates. Air emissions in DC from NAICS 2211 are likely to be much lower than suggested by the District s share of retail sales revenue, particularly when compared to states having similar revenue but which generate a larger share of the electricity they consume or are net exporters. Net Generation by Energy Source by Type of Producer and State where net generation represents the total electrical output net of station service, and Retail Price of Electricity to Ultimate Customers by End-Use Sector and State. This assumes that all electricity generated within a state is valued at the retail price of electricity consumed within the state, regardless of whether some part of the state s electricity generation is exported to other states, or whether the state imports a substantial amount of electricity consumed from other states. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 24

32 Natural Gas Sector (NAICS 2212) The natural gas distribution sector (NAICS 2212) comprises: (1) establishments primarily engaged in operating gas distribution systems (e.g., mains, meters); (2) establishments known as gas marketers that buy gas from the well and sell it to a distribution system; (3) establishments known as gas brokers or agents that arrange the sale of gas over gas distribution systems operated by others; and (4) establishments primarily engaged in transmitting and distributing gas to final consumers. Note that it includes neither the production of natural gas, which is instead captured under NAICS 2111, nor pipeline transportation of that gas, which is captured under NAICS The national revenue reported for NAICS 2212 was apportioned to the states on the basis of the retail value of natural gas distributed within each state. EIA reports the volume of natural gas distributed to end-use customers in 2002, by state. It also reports data on the retail price for natural gas by state and by customer type in We estimated the state-level retail value of natural gas delivered to customers by multiplying the EIA-reported total quantity of natural gas by the estimated average retail price for natural gas sales within the state. 26 Water Sector (NAICS 2213) The Water, Sewage and Other Systems Sector (NAICS 2213) includes the following sub-sectors and activities: Water Supply and Irrigation Systems. This industry comprises establishments primarily engaged in operating water treatment plants and/or operating water supply systems. The water supply system may include pumping stations, aqueducts, and/or distribution mains. The water may be used for drinking, irrigation, or other uses. (Note that approximately 80 percent of the national revenue reported for sector 2213 comes from this sub-sector.) Sewage Treatment Facilities. This industry comprises establishments primarily engaged in operating sewer systems or sewage treatment facilities that collect, treat, and dispose of waste Steam and Air-Conditioning Supply. This industry comprises establishments primarily engaged in providing steam, heated air, or cooled air. The steam distribution may be through mains. The national revenue reported for NAICS 2213 was apportioned to the states on the basis of each state s estimated public supply use of water, as reported by the USGS for Since the public water supply infrastructure accounts for a significant share of permitted discharges captured in TEAM through the inclusion of EPA s Permit Compliance System (see Section 4.1), using USGS data on public supply water We obtained data on the volume of natural gas distributed to customers by state for EIA reports natural gas prices by state and by customer type. We calculated an average retail price for natural gas by state by averaging prices for residential, commercial, industrial, transportation, and electric power users, weighted by the quantity delivered to each use category. EIA does not provide a price for transportation users for 13 states, even as it provides volume delivered to this same group of users in these states. In those cases, we used the same price charged to commercial users since they appeared to generally be similar, and since until 1997 EIA grouped transportation and commercial uses together. Another approach would have involved using state revenue reported in the 1997 Economic Census to disaggregate 2002 national revenue. The 1997 Economic Census, however, suppresses revenue for 22 states (28 percent of national revenue). For these states, we would have had to estimate state revenue based on population. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 25

33 use to apportion economic value to the states is expected to most adequately characterize the level of activity within NAICS 2213 that generated the water discharges. 3.2 Agricultural Census The U.S. Department of Commerce Economic Census excludes the agricultural sector (NAICS 11). Economic data for this sector are instead compiled and distributed by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), following the same schedule as the U.S. Census. The TEAM baseline data set was developed based on the 2002 Census of Agriculture. 28 Table 59 Summary by North American Industry Classification System: 2002 provides data on the number of farms and market value of agricultural products sold and agricultural payments ($1,000) by NAICS code. The Census of Agriculture provides data for crop and animal production activities, classified as NAICS sectors 111 and 112, respectively. It does not provide data on Forestry and Logging (NAICS 113), Fishing, Hunting and Trapping (NAICS 114), and Support Activities for Agriculture and Forestry (NAICS 115). Since the Economic Census also excludes these sectors, they are not represented in the final TEAM baseline dataset (see Section 3.3.1) and TEAM analyses correspondingly do not consider the environmental impacts associated to these sectors National Data The agricultural data are comprised of the eleven 4-digit NAICS sectors that encompass crop production (NAICS 111) and animal production (NAICS 112), as listed in Table 3-5. National level data are available directly for all eleven agricultural sectors and were simply compiled for use in TEAM. 29 Table 3-5. Economic sectors covered in the 2002 Census of Agriculture NAICS Description 111 Crop production 1111 Oilseed and grain farming 1112 Vegetable and melon farming 1113 Fruit and tree nut farming 1114 Greenhouse, nursery, and floriculture production 1119 Other crop farming 112 Animal production 1121 Cattle ranching and farming 1122 Hog and pig farming 1123 Poultry and egg production 1124 Sheep and goat farming 1125 Animal aquaculture 1129 Other animal production Quick Stats Version 1.2. Accessed on July 29, NAICS sector 1112 has data provided for 5-digit sector Since there is only one 5-digit sector for the parent 1112, we simply set the value for 1112 as equal to that provided for Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 26

34 3.2.2 State Data The state data were processed similarly as the Economic Census data described in Section All states have records for each of the eleven 4-digit NAICS agricultural sectors. In some cases, however, USDA suppressed the revenue value to avoid disclosing individual business information. In these cases, we followed an approach similar to that used for the Economic Census, i.e., we estimated suppressed entries by disaggregating the residual value provided for the parent 3-digit NAICS sector in proportion to the relative national share of the 4-digit NAICS sectors. 30 No data were suppressed at the 3-digit NAICS level and therefore it was not necessary to disaggregate data based on revenue reported at the 2-digit level. Table 3-6 presents the number of records obtained directly from the Census of Agriculture and those estimated from parent sectors. As shown in the table, a very small share of the TEAM state-level baseline data was suppressed and had to be estimated. Table 3-6. Incidence of estimated revenue values in TEAM state baseline file (sectors reported in Census of Agriculture. Number of records Fraction of revenue Revenue data are provided. Reported value is used in TEAM % Revenue data are suppressed. Value is estimated for use in TEAM by disaggregating value reported by 3-digit NAICS % Total % Even after applying the various estimation methodologies described above, we were unable to assign revenue to 497 combinations of state/4-digit NAICS because of insufficient information on revenue for a higher NAICS sector within the state. These missing values represent approximately 4 percent of the number of observations in the source datasets. Sectors affected by the missing data are summarized in Table 3-7. The exhibit also presents the number of states for which revenue values are missing. 30 There are relatively few suppressed data points at the 4-digit NAICS and state level. The 23 suppressed data points are found in only seven states and reflect low levels of agricultural production in the state. Suppressed data include, by state (and indicating the number of data points suppressed, and type of agricultural activity): Alaska (2, crop production), Arizona (3, animal production), Delaware (2, animal production), Nevada (6, both crop and animal production), North Dakota (4, both crop and animal production), Washington (3, animal production), and Wyoming (3, crop production). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 27

35 Table digit NAICS Sectors for which Revenue Values Could not be Estimated for TEAM NAICS Sector Description Number of States with Missing Revenue 2131 Support Activities for Mining Motion picture and video industries Sound recording industries Internet publishing and broadcasting Wired telecommunications carriers Wireless telecommunications carriers (except satellite) Telecommunications resellers Satellite telecommunications Cable and other program distribution Other telecommunications Other information services Monetary authorities - central bank Depository credit intermediation Nondepository credit intermediation Activities related to credit intermediation Securities and commodity contracts intermediation and brokerage Securities and commodity exchanges Other financial investment activities Insurance carriers Agencies, brokerages, and other insurance related activities Other investment pools and funds (part) Offices of real estate agents and brokers Activities related to real estate Automotive equipment rental and leasing Consumer goods rental Commercial and industrial machinery and equipment rental and leasing Lessors of nonfinancial intangible assets (except copyrighted works) 2 TOTAL Overview of TEAM Economic Baseline Summary of TEAM Economic Baseline Datasets The national TEAM economic baseline data file provides either reported or estimated revenue for digit NAICS economic sectors. The state TEAM economic baseline data file provides reported or estimated revenue for 13,019 states/4-digit NAICS combinations across 286 NAICS4 sectors. Revenue data for the following sectors in the state dataset are missing: 5121, 5122, 5171, 5172, 5175, 5221, and 5241 (see Section for a discussion of missing state data). Appendix B provides more detail on the data available at the state and national levels in terms of total revenue by 4-digit NAICS sector. The Appendix also indicates, for each 4-digit NAICS sector, the fraction of state revenue that were estimated for use in TEAM and the fraction of revenue reported at the national level that are accounted for by state revenue. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 28

36 3.3.2 Comparison of TEAM Economic Baseline with Revenue Reported in Census Table 3-8 compares, at the level of 2-digit NAICS sectors, national revenue values reported in the 2002 Economic Census and Census of Agriculture with the sum of state level estimated and reported revenue for 4-digit NAICS sectors. As was discussed in Sections and 3.3.1, a number of sectors have incomplete coverage at the state level. In particular, substantial shares of revenue are missing for sectors within NAICS 51 and 52. However, the omission of these sectors should not adversely affect analyses of environmental impacts using TEAM since these sectors are not expected to have significant air emissions, water discharges or energy use, as compared to other sectors of the economy. Table 3-8. Comparison of Reported National Revenue and the Sum of Estimated/Reported State Values, by 2-digit NAICS sector 2-digit National Total Revenue from Fraction of National Sum of 4-digit/State Revenue in NAICS Economic Census or Census of Total Revenue TEAM Economic Baseline ($) Sector Agriculture ($) Represented in TEAM ,646,355, ,646,353, % ,911,093, ,907,436, % ,907,044, ,907,044, % 23 1,196,555,587,000 1,209,852,095, % ,916,136,712,000 3,918,232,139, % 42 4,634,755,112,000 4,634,755,112, % ,056,421,997,000 3,056,421,997, % ,152,040, ,880,357, % ,845,956, ,506,663,000 47% 52 2,803,854,868, ,230,865,000 29% ,587,706, ,782,218,000 99% ,801,038, ,886,917, % ,064,264, ,062,535, % ,577,580, ,854,706, % 61 30,690,707,000 30,702,972, % 62 1,207,299,734,000 1,207,233,851, % ,904,109, ,908,353, % ,498,718, ,498,718, % ,049,461, ,027,249, % 3.4 NERC-Adjusted Economic Baseline An alternative set of baseline data and input files have been developed to support the analysis of the impacts of electricity-consuming sectors based on the specific regional profile of each sector s operations and the GHG emissions intensity of electricity produced within each NERC region. The TEAM economic baseline was adjusted to account the NERC-specific electrical power sectors as follows: State-level economic activity: States corresponding to each NERC region are identified using the state-to-nerc mapping (Table A-1 in Appendix A). The NAICS sector identified for each entry is then reassigned to the appropriate NERC-specific NAICS sector. Consequently, instead of being reported for the total electric power sector (NAICS 2211), economic activity in the new TEAM NERC-adjusted state-level economic activity data file is now reported for each NERC-specific electric power sector (221X). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 29

37 National-level economic activity: National-level economic activity data for the electric power sector is subdivided into the ten NERC regions using allocation factors developed based on the TEAM state-level economic baseline. As the result of this allocation, instead of being reported for the total electric power sector (NAICS 2211), economic activity in the new TEAM NERC-adjusted state-level economic activity data file is now reported for each NERC-specific electric power sector (221X). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 30

38 Chapter 4: Emissions Baseline Data The second major component of the baseline data used in TEAM is the emissions and resource use data associated with each economic sector. TEAM considers a multitude of environmental impacts associated with both stationary and mobile pollution sources: water discharges, air emissions, energy use and their resulting carbon emissions. The emissions and resource use data are used in conjunction with economic baseline data to calculate implicit emission factors, which are used in turn to estimate emissions resulting from the modeled scenario. This section discusses the data sources and methodology used to develop the emissions and resource use dataset for the three principal media types: Water Discharges (Section 4.1); Air Emissions (Section 4.2); and Energy Use and Carbon Emissions (Section 4.3). 4.1 Water Discharges TEAM includes baseline effluent discharge data for both direct and indirect discharges. The primary source of data on direct dischargers is the EPA Permit Compliance System (PCS), which reports facility-level information on annual discharges of pollutants by holders of National Pollutant Discharge Elimination System (NPDES) permits. The Toxics Release Inventory (TRI), which also reports annual discharges by pollutants at the facility level, provided data for indirect water discharges and additional data on direct water discharges to complement PCS data. The sections below describe the processing of the PCS data (Section 4.1.1) and TRI data (Section 4.1.2) for use in TEAM PCS Data PCS is a data management system maintained by EPA s Office of Enforcement and Compliance Assistance (OECA). It is used to track permit, compliance, and enforcement status on facilities regulated under the Clean Water Act s National Pollutant Discharge Elimination System (NPDES) program. The NPDES program controls water pollution by regulating point sources that discharge pollutants directly into waters of the United States 31. More than 65,000 industrial facilities and municipal wastewater treatment plants currently hold NPDES permits, including 6,400 facilities that are classified as major dischargers based on toxic pollutant potential, flow/stream flow volume, conventional pollutant loading, public health impacts, water quality factors, and proximity to coastal waters. These major dischargers must demonstrate compliance with NPDES permit limits by submitting monthly Discharge Monitoring Reports (DMRs) to the permitting authority, including measured pollutant concentration or quantity, water quality parameters (e.g., dissolved oxygen and temperature), and flow. EPA in turn uses reported pollutant concentration/quantity and flows to estimate annual pollutant loadings for each NPDES permit holders. 31 These sources are those that discharge directly into receiving water bodies, as opposed to sources that discharge to publicly owned treatment works (POTWs), or more diffuse non-point sources (e.g., many agricultural sources) that discharge directly to U.S. waters but that are not currently subject to NPDES permitting. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 31

39 Data Source We obtained 2002 PCS annual loadings data from EPA s Office of Water. Derived from the primary PCS data, the PCS annual loading data were compiled to support the Office of Water s 2005 annual screeninglevel analysis of effluent limitations guidelines and standards. 32 These data were subjected to extensive review, cleaning, and revisions by EPA and are therefore considered superior for this application to the primary PCS data that are available for download through EPA s data portal. In compiling the annual loading estimates, EPA further investigated (and sometimes contacted directly) facilities found to represent an inordinate share of a given economic sector s total toxic-weighted water discharges. 33 EPA thus identified and corrected data-entry errors (such as erroneous concentration units), and corrected invalid or incorrect SIC economic sectors to better reflect a facility s economic activity. EPA also adjusted the estimated annual loadings to account for intermittent discharges, intake pollutants and double-counting of internal monitoring points. Finally, EPA improved the methodology used for estimating annual loading based on reported monthly concentrations and flows. 34 The PCS-derived database (PCSLoad2002) provides estimated annual loading for 24,669 permitted facilities and provides, for each observation, the facility ID (i.e., the facility s NPDES permit number), SIC economic sector, parameter, and pounds discharged annually. The database also provides, for parameters representing discrete chemical compounds, the corresponding Chemical Abstract Number (CAS) and toxic weighting factor Processing of PCS Data for Use in TEAM The key steps involved in processing the PCS loadings data for use in TEAM are described below. Step 1: Select Relevant Pollutant Parameters PCSLoad2002 includes measurements for all parameters the permitted facilities were required to report on. Not all PCS parameters, however, represent discrete pollutants; they also include water quality indicators, operational metrics, qualitative monitoring metrics, or broadly-described pollutant types. Figure 4-1 ranks the thirty most frequently reported parameters in the PCS database, out of a total of 950 parameters The data were obtained as a single database file (PCSLoad2002_v04.mdb) from the EPA Office of Water contractor that assisted EPA in the 2005 review (Eastern Research Group (ERG)). For more information about this effort, see Final Effluent Guidelines Plan for 2006 discussed in EPA ranked facilities according to toxic-weighted pounds released and used these ranking to identify facilities with unusually high reported discharges relative to other facilities in an industrial category. EPA contacted these facilities to verify their reported releases. EPA also made corrections to the data after receiving facility-specific comments during the rulemaking process. For example, EPA used a hybrid approach to calculate loadings whereby it assumes that concentrations reported as below the detection limit (DL) are zero if the pollutant concentration never exceeds the DL value at a given discharge point during the reporting year (i.e., if that pollutant was never detected in any of the samples collected from the discharge point), while it assumes concentration equal to half the DL value for any undetected compound if the same pollutant has been measured above DL at least once during the year. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 32

40 Figure 4-1: Most frequently reported PCS parameters (based on non-zero annual loadings) Count of non-zero annual loadings reported 20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 - Total Suspended Solids BOD5 Nitrogen, Ammonia Oxygen, Dissolved (DO) Chlorine Oil & Grease Copper Zinc Phosphorus Lead Nickel Iron Chromium Cadmium Nitrogen, Kjeldahl Total (As N) Chemical Oxygen Demand Mercury Cyanide Nitrogen, Total (As N) Phenol & Phenolics Hardness, Total (As CaCO3) Total Dissolved Solids Nitrite Plus Nitrate Total 1 Det. (As N) PCS Parameter Arsenic Ammonia Chloride Carbon, Tot Organic (TOC) Silver Aluminum ph We excluded from the TEAM baseline certain PCS parameters for which calculating increases in discharge quantity may be conceptually inappropriate or would not provide information useful for TEAM modeling purposes. Examples of parameters a priori excluded from the TEAM baseline are provided in Table 4-1. Some chemical compounds are reported in different forms in PCS (e.g., dissolved copper, total copper). In compiling loading data for its screening analysis of effluent guidelines, EPA calculated a single annual loading value for each group, where appropriate, by combining or selecting entries for the same chemical compound, based on data precedence rules described in the study methodology. 35 The data used in compiling the TEAM baseline reflects this EPA-calculated combined loading. 35 For example, data provided for total copper takes precedence over data for copper. If total copper is not reported, data for copper takes precedence over data provided for total recoverable copper. EPA then uses the following order of precedence for remaining forms: potentially dissolved copper, then dissolved copper or suspended copper. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 33

41 Table 4-1: Parameters excluded from TEAM baseline data selection. Parameter Type / Group Examples of parameters General compound groups Oil and grease, surfactants, free available oxidants, total agg. concentration Degradation potential Water quality indicators Temperature Biological indicators Flow or stream Others Chemical oxygen demand (COD), biochemical oxygen demand (BOD) ph, alkalinity, acidity, acid compounds, salinity, conductivity, turbidity, hardness, specific conductance, color Temperature, temperature difference between intake and discharge Fecal coliforms, e.coli, human enteric virus, toxicity units Flow rate, rainfall, tide stage, depth to water level Plant capacity factor, dilution factor, fish on hand, duration of discharge, length of longest ph excursion, overflow use occurrences, paper production, ultraviolet light intensity, COD % removal Step 2: Classify Discharges According to NAICS Sectors No SIC code was provided for 246 facilities included in EPA s dataset. Wherever possible, we determined a SIC code for these facilities using one of the following sources: (i) the SIC code identified in TRI if the facility is a TRI reporter (see Section for a description of the TRI data set); (ii) sector information available through EPA s Envirofacts Facility Registry System (FRS) 36, or (iii) the facility name if it indicates a clear and unequivocal SIC code (e.g., SIC 4952 if facility name contains WWTP ). Using these other sources, we were able to fill in SIC codes for 141 facilities. At the end of this effort, the dataset still contained 105 facilities for which a SIC code could not be determined. Direct discharge data for these facilities are not included in the final TEAM dataset. The impact of excluding these facilities is discussed in Section During our review of the data, we noted 206 facilities assigned to SIC Code 9999: Non-Classifiable Establishments. We assigned more specific SIC codes to 113 of these facilities by using information available in TRI and Envirofacts. We also used information available in Envirofacts to reassign four facilities originally assigned to SIC Code 8744: Facilities Support Services to more specific sectors, namely, hazardous waste treatment, national security, ammunition, and crude petroleum and natural gas. Finally, we noted a number of facilities assigned to SIC code 4952: Sewerage Systems for which this classification seemed to reflect only the portion of the facility that holds the NPDES permit (e.g., wastewater treatment operations) rather than the facility s primary economic activity. For example, of 11,426 facilities originally classified in SIC 4952, we identified 48 facilities for which TRI provides a different primary SIC sector (e.g., manufacturing or national security sectors). For these facilities, we replaced the PCS-provided SIC code with that provided in TRI. 36 Envirofacts Facility Registry System queries are accessible at FRS lists the SIC (and NAICS) codes assigned to a facility under various EPA programs Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 34

42 We then applied a SIC-NAICS crosswalk to this adjusted PCS annual loadings dataset to map each discharge classified by SIC economic sector to the corresponding NAICS sector(s). We developed the SIC-NAICS crosswalk based on the Census Bureau s concordance between SIC 1987 and NAICS 2002 sector classification systems, 37 with relative weights calculated based on TEAM s 2002 state-level economic baseline (developed from the 2002 Economic Census). Figure 4-2 illustrates the concept. In cases where more than one NAICS sector corresponds to a SIC sector, the discharge amount is distributed among the 4- digit NAICS sectors in proportion to the relative revenue reported for each of the NAICS sectors at the state level. Using these state-specific weights offers the advantage of preventing discharges from potentially being assigned to NAICS sectors that report no economic activity within the state, as these discharges would not be included in a TEAM analysis, 38 while apportioning emissions according to the relative scale of economic activity within the possible sectors. Two sectors -- the logging sector (SIC: 2411 NAICS: 1133) and the rail transportation sector (SIC: 4011 NAICS: 4821) are not included in the TEAM economic baseline dataset because they are outside the scope of the Economic Census; yet these sectors report significant water discharges. We included these discharges in the TEAM emissions baseline data file under their respective NAICS codes, even though they will not be reflected in the TEAM analysis. This would enable EPA to eventually include these emissions in TEAM analyses by adding the sectors respective revenue to the TEAM economic baseline For a list of NAICS sectors corresponding to SIC codes, see For a discussion of the state-level economic baseline dataset, refer to Chapter 3. Also for this reason, we do not include, in compiling PCS data for use in TEAM, discharges reported by facilities in Puerto Rico, the Virgin Islands, and Guam, as there is no corresponding economic activity data in TEAM s economic baseline file. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 35

43 Figure 4-2. SIC-NAICS crosswalk for assigning direct discharges to 4-digit NAICS sectors. State-specific weights SIC NAICS CA MS* OH Weft Knit Fabric Mills Fabric Mills Textile and Fabric Finishing and Fabric Coating Mills Use of SIC-NAICS Crosswalk SIC Annual Loadings of Zinc Example: A facility located in California in SIC sector 2257 discharges 100 lb/year of zinc. This facility is assumed to discharge 37.9 lb/year of zinc for NAICS sector 3132; and 62.1 lb/year for NAICS sector State CA MS lbs 200 lbs NAICS Fabric Mills Textile and Fabric Finishing and Fabric Coating Mills Fabric Mills Textile and Fabric Finishing and Fabric Coating Mills Annual Load 37.9 lbs 62.1 lbs lbs -- * No economic activity reported in Mississippi for NAICS sector All discharges associated with SIC sector 2257 would therefore be attributed to NAICS sector 3132 in Mississippi TEAM PCS Data Coverage The final PCS-derived annual loadings dataset compiled for use in TEAM comprises 105,975 observations. These observations are distributed among 249 different NAICS sectors. Table 4-2 provides summary statistics for the 30 NAICS sectors with the largest number of direct water discharges. Sector 2213: Water, Sewage, and Other Systems has by far the largest number of direct water discharges reported. Most facilities classified within this sector are POTWs. The sector also seems to include, however, some facilities associated with industrial operations (e.g., manufacturing plants). Mapping direct water discharges from SIC to NAICS resulted in the exclusion of 3,309 non-zero observations due to either invalid/missing SIC codes or the absence of economic activity for the NAICS sector(s) within the state (including sectors that were outside the scope of the Economic Census such as the U.S. Postal Service). Excluded discharges represent approximately 3 percent of the observations in the original data source, but account for only 0.1 percent of the total annual load reported, and less than 0.0 percent of the total toxic-weighted annual load. The TEAM baseline data therefore captures almost the entirety of estimated PCS annual loadings for Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 36

44 Table 4-2: Summary of PCS Direct Water Discharge Data for Use in TEAM. Rank NAICS Description Number of Facilities Number of Parameters Number of Observations Water, Sewage and Other Systems 11, , Basic Chemical Manufacturing , Other Chemical Product and Preparation Manufacturing , Electric Power Generation, Transmission and Distribution , Resin, Synthetic Rubber, and Artificial Synthetic Fibers ,811 and Filaments Manufacturing Remediation and Other Waste Management Services , Petroleum and Coal Products Manufacturing , Waste Treatment and Disposal , Lessors of Real Estate , Alumina and Aluminum Production and Processing , Oil and Gas Extraction , Direct Selling Establishments , Pulp, Paper, and Paperboard Mills , Steel Product Manufacturing from Purchased Steel , Petroleum and Petroleum Products Merchant Wholesalers , Iron and Steel Mills and Ferroalloy Manufacturing Coating, Engraving, Heat Treating, and Allied Activities Land Subdivision Nonmetallic Mineral Mining and Quarrying Other Food Manufacturing Gasoline Stations Animal Slaughtering and Processing RV (Recreational Vehicle) Parks and Recreational Camps Nonferrous Metal (except Aluminum) Production and Processing Pesticide, Fertilizer, and Other Agricultural Chemical Manufacturing Metal Ore Mining Textile and Fabric Finishing and Fabric Coating Mills Other Professional, Scientific, and Technical Services Scientific Research and Development Services Wholesale Electronic Markets and Agents and Brokers Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 37

45 4.1.2 TRI Water Dischargers Data The Toxics Release Inventory (TRI) is the common name for the data compiled under Section 313 of the Emergency Planning and Community Right-to-Know Act (EPCRA). Under this statute, facilities that meet certain criteria must report their releases and other waste management activities for listed toxic chemicals by filing a report for each chemical for which they exceed the reporting threshold. The TRI list of chemicals for reporting year 2002 includes more than 600 chemicals and chemical categories. A facility must report to TRI if it meets the following criteria: Economic sector: Facility is classified in SIC codes 20 through 39 (i.e., manufacturing sector), seven additional SIC codes, or is a federal facility. The primary SIC code for the facility is typically associated with the largest source of a facility s revenues. Number of Employees: Facility has 10 or more full-time employees or equivalent. Activity/Quantity Threshold: Facility manufactures, processes, or otherwise uses each TRI chemical at or above the appropriate activity threshold. Reporting thresholds are not based on the amount of release but rather on the level of activity. Facilities report annual loads released to the environment of each toxic chemical or chemical category that meets reporting requirements, including releases to receiving streams (i.e., direct discharges) and transfers to off-site locations such as discharges to POTWs (i.e., indirect discharges). Facilities are not required to sample and analyze waste streams to determine the quantity of chemicals released but may estimate releases based on mass balance calculations, published emission factors, site-specific emission factors, or other approaches. In its guidance, EPA notes that a facility should use half the detection limit to estimate mass releases of chemicals that are measured below the detection limit but are reasonably expected to be present TRI Data Source We obtained TRI data from the same source as the PCS annual loadings data discussed in Section EPA s Office of Water also used 2002 TRI data in its screening level analysis of effluent guidelines. Similarly to the PCS data, EPA reviewed the TRI data, made corrections as needed based on the results of this review, and calculated toxic-weighted annual loadings from annual loading values provided by TRI reporters Processing of TRI Data for Use in TEAM The TRIReleases2002 data includes pounds of TRI chemicals released by TRI reporters through direct discharges (12,899 records) and indirect discharges (13,589 records) during The releases are classified according to each facility s primary SIC economic sector. Our review of the SIC code assignments Non-detects of dioxin and dioxin-like compounds may be reported as zero. The data were obtained from ERG in a database format (TRIReleases2002_v04.mdb) The dataset represents 26,488 TRI reported direct discharges to streams or releases to POTWs out of a total of 92,955 Form 1 reports submitted to the TRI program. The remaining TRI Form 1 reports only indicate releases to air or other non-water media (e.g., solid waste) and are therefore not included in the Office of Water s dataset. Additionally, 25 reports contain discharges of unspecified chemicals (Trade Secret chemical or mixture ) and are also not included. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 38

46 identified 72 facilities with no SIC code, and two facilities assigned to invalid SIC. We determined valid SIC codes for 71 of these facilities based on information available in Envirofacts. We then applied the same crosswalk used for PCS loadings (see Section ) to map TRI discharges to the appropriate 4-digit NAICS economic sectors. For indirect discharges, EPA provides both the total pounds released by the facility and adjusted pounds released to the environment after accounting for pollutant removal that occurs at the POTW prior to discharge to the receiving stream. For this application, we used data on the total pounds released by each facility since TEAM is interested in the aggregate amount of pollution generated by the economic activity, irrespective of treatment or further processing that may be accomplished outside the facility. As noted above, the TRI dataset also provides direct water discharge data. In theory, the annual loading should be similar to those reported in PCS for facilities/chemicals that are common to the two systems. A comparison of TRI data with PCS data for 1,950 records over which the two datasets overlap, however, shows significant differences in annual loadings. Thus, while 70 percent of PCS annual loadings are less than or equal to the corresponding TRI loadings, about 20 percent of PCS values are more than twice the corresponding TRI values, and about 1 percent of PCS values are more than 100 times greater than the corresponding TRI values. Additionally, the TRI dataset often indicates non-zero loadings for chemicals reported with zero values in PCS. Since PCS reflects actual measurements and TRI may be based on more conservative estimates, 42 we used TRI direct water discharge data only when no corresponding discharge is reported in PCS for the same facility/chemical. 43 TRI direct discharge data is therefore used to complement PCS data in TEAM. The original source of each data point is identified in the TEAM baseline dataset as either PCS or TRI TEAM TRI Data Coverage Indirect Water Discharges The final TRI-derived loading dataset compiled for use in TEAM comprises 24,043 indirect water discharge observations. These observations are distributed among 128 different NAICS sectors. Table 4-3 provides summary statistics for the 30 NAICS sectors with the largest number of indirect water discharges. Mapping indirect water discharges from SIC to NAICS resulted in the exclusion of 63 facilities (including 49 federal facilities) and representing 198 non-zero observations, due to either invalid/missing SIC codes or the absence of economic activity for the NAICS sector(s) within the state. These excluded discharges represent approximately 0.3 percent of the observations in the data set, but account for only 0.04 percent of the total annual load, and 0.7 percent of the total toxic-weighted annual load As EPA notes, TRI encourages facilities to report some compounds as present at one-half the detection level if a facility suspects that the compound has the potential to be present, even if measured data show the compound is below the detection level. As a result, many companies are conservative and adopt this approach Annual Screening-Level Analysis: Supporting the Annual Review of Existing Effluent Limitations Guidelines and Standards and Identification of Potential New Categories for Effluent Limitations Guidelines and Standards, EPA Report 821-B , August 2005, page 3-4. In its screening level analysis of effluent guidelines, the Office of Water did not pass judgment on the relative value of PCS versus TRI data; it used both data sources to rank the relative contribution of various point source categories. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 39

47 Table 4-3: Summary of TRI Indirect Water Discharge Data for Use in TEAM. Ran k NAICS Description Number of Facilities Number of Parameters Number of Observations Basic Chemical Manufacturing , Other Chemical Product and Preparation Manufacturing , Coating, Engraving, Heat Treating, and Allied Activities , Motor Vehicle Parts Manufacturing , Motor Vehicle Body and Trailer Manufacturing , Semiconductor and Other Electronic Component Manufacturing Other Fabricated Metal Product Manufacturing Alumina and Aluminum Production and Processing Paint, Coating, and Adhesive Manufacturing Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments Manufacturing Petroleum and Coal Products Manufacturing Boiler, Tank, and Shipping Container Manufacturing Nonferrous Metal (except Aluminum) Production and Processing Steel Product Manufacturing from Purchased Steel Other General Purpose Machinery Manufacturing Forging and Stamping Other Miscellaneous Manufacturing Other Food Manufacturing Motor Vehicle Manufacturing Office Furniture (including Fixtures) Manufacturing Foundries Oil and Gas Extraction Other Transportation Equipment Manufacturing Other Electrical Equipment and Component Manufacturing Plastics Product Manufacturing Soap, Cleaning Compound, and Toilet Preparation Manufacturing Cutlery and Handtool Manufacturing Pharmaceutical and Medicine Manufacturing Rubber Product Manufacturing Dairy Product Manufacturing Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 40

48 Direct Water Discharges As discussed in Section , TRI also provides direct water discharge data that may complement the PCS data by providing annual loadings for chemicals not measured under the NPDES program. The TRI dataset compiled for use in TEAM provides 17,474 direct water discharge observations (these add to the 105,975 observations obtained from PCS). TRI direct water discharges are reported for 107 NAICS sectors. Table 4-4 provides summary statistics for the 30 NAICS sectors with the largest number of direct water discharges. Table 4-4: Summary of TRI Direct Water Discharge Data for Use in TEAM. Rank NAICS Description Number of Number of Number of Facilities Parameters Observations Electric Power Generation, Transmission and Distribution , Petroleum and Coal Products Manufacturing , Pulp, Paper, and Paperboard Mills , Basic Chemical Manufacturing , Other Chemical Product and Preparation Manufacturing , Steel Product Manufacturing from Purchased Steel Alumina and Aluminum Production and Processing Iron and Steel Mills and Ferroalloy Manufacturing Petroleum and Petroleum Products Merchant Wholesalers Direct Selling Establishments Nonferrous Metal (except Aluminum) Production and Processing Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments Manufacturing Motor Vehicle Parts Manufacturing Foundries Coating, Engraving, Heat Treating, and Allied Activities Other Fabricated Metal Product Manufacturing Pesticide, Fertilizer, and Other Agricultural Chemical Manufacturing Oil and Gas Extraction Sawmills and Wood Preservation Motor Vehicle Body and Trailer Manufacturing Other Electrical Equipment and Component Manufacturing Other Food Manufacturing Remediation and Other Waste Management Services Waste Treatment and Disposal Other Miscellaneous Manufacturing Other General Purpose Machinery Manufacturing Paint, Coating, and Adhesive Manufacturing Boiler, Tank, and Shipping Container Manufacturing Rubber Product Manufacturing Forging and Stamping Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 41

49 Data Limitations Neither PCS nor TRI represent an exhaustive, perfect source of data on water discharges. Both data source shave limitations which are inherited by TEAM. Regarding PCS data: Pollutant coverage. PCS contains data only for pollutants that a facility is required to monitor, as specified in its NPDES permit; a facility is not necessarily required to monitor all pollutants discharged. Facility coverage. Discharge monitoring data are provided primarily for the 6,400 major dischargers, over 60 percent of which are classified as sewage treatment facilities (i.e., POTWs). Many smaller direct dischargers listed in PCS hold so-called General, instead of Standard, NPDES permits. PCS includes only limited discharge monitoring data (either for actual discharges of discharge limits) from minor dischargers for non-conventional pollutants (i.e., pollutants other than biological oxygen demand, total suspended solids, oil and grease, etc.) Economic sector identification. Facilities only provide SIC code information for their primary operations, even though the data may represent other operations as well. Conversely, some facilities seem to provide SIC codes for operations that generate the effluent, but this SIC code may be different from the facility s main economic activity (e.g., waste treatment operations within a manufacturing plant). Additionally, SIC codes are missing altogether for a few facilities. While we were able to address some of these issues as we compiled the TEAM baseline dataset, we suspect that a number of questionable sector assignments remain. Reviewing and correcting the economic sector assignment of all facilities would be a time consuming task. 44 Reported data. The availability of actual discharge data as opposed to discharge limits is variable. As noted in the methodology discussion above, the reported discharge limits was used as an estimate of actual discharge values for facilities with missing discharge data. This approach may lead to overestimation of facility discharges. Data quality issues. PCS data are entered manually, which may lead to data entry errors. The system does not provide a consistent means of verifying that the data entries are accurate and reasonable. Facilities report pollutant concentration/quantities rather than annual loading. While it is possible to estimate loading by combining the concentration with the reported flowrate, this calculation assumes that the pollutant is discharged continuously at the reported concentration and flowrate. In some cases, the reported flow rates may include stormwater and non-contact cooling water, as well as process wastewater. Using the total flowrate to calculate pollutant loadings based on concentration may overestimate loadings. Additionally, some discharges occur intermittently and assuming a 24- hr/7-day per week discharge results in overestimating of the annual pollutant loading. While EPA addressed some of these issues and corrected data during its review of the PCS data, these corrections focused primarily on outlier facilities that reported relatively greater toxic-weighted annual loads than facilities in the same economic sector; data for other facilities were not corrected. The values reported for parameters that do not have toxic effects (e.g., suspended solids) are unlikely to have been corrected. 44 One option may be to review facilities within selected sectors, for example reviewing the list of sewage treatment facilities to identify, and reassign, facilities that are not POTWs. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 42

50 Regarding TRI data: Economic sector coverage. Only facilities in a specified list of SIC codes are required to report. The list covers manufacturing facilities, certain metal mining facilities, certain coal mining facilities, certain electrical utilities, hazardous waste treatment and disposal facilities, chemical and allied product wholesale distributors, petroleum bulk plans and terminals, and solvent recovery services. While broad, the list does not cover all industry sectors and some sources of water pollutant discharges are therefore not included. Facility coverage. Small establishments (less than 10 employees) are not required to report to TRI, nor are facilities that do not meet the reporting thresholds for listed chemicals. TRI reporters may therefore represent only a subset of a given industry representing relatively larger facilities. Uncertainty in reported amounts. Release reports are, in part, based on estimates rather than actual measurements. EPA has found that many companies conservatively assume a default value equal to half the detection limit for all compounds that may be present even when these compounds have not been measured in their discharges. For facilities with large flows, this can result in large estimated total loadings. In using the water discharge data in TEAM, users need to keep in mind the fact that reporting requirements may not be uniform in terms of chemicals that must be measured and reported across facilities, even within the same program, as highlighted by the PCS data. This has implications for any effort to compare annual loadings across industries on anything more aggregate than on a per chemical basis. 4.2 Air Emissions The TEAM air emissions baseline was developed based on EPA s 2002 National Emissions Inventory (NEI), which contains emissions data for area, mobile and point sources for criteria and hazardous air pollutants. 45 NEI covers emissions of criteria air pollutants (CAPs) and hazardous air pollutants (HAPs) for all areas of the United States. For this effort, we used the final version (version 3) of 2002 NEI point sources emissions data released by EPA in September The NEI is organized into three primary components area, mobile, and point. Each of these components is in turn organized into two datasets, one each for CAPs and HAPs, as described in Table The National Emission Inventory database is the successor of two separate databases that had previously been maintained by EPA for emissions of criteria air pollutants (National Emission Trends database, or NET) and hazardous air pollutants (National Toxics Inventory database, or NTI). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 43

51 Table 4-5: NEI Components and their Respective Pollutants. Reporting thresholds for mobile source CAPs are specified under the Consolidated Emissions Reporting Rule (CERR). CAPs are the only air pollutants with national air quality standards that define allowable concentrations of these substances in ambient air. The data for the following four CAPs and three precursors/promoters of CAPs are reported in NEI: Criteria Air Carbon monoxide (CO) Pollutants (CAPs) 47 Particulate matter less then 2.5 microns (PM2.5) Particulate matter less then 10 microns (PM10) Hazardous Air Pollutants (HAPs) The NEI also includes emission estimates for additional compounds that are precursors or promoters of CAPs: Ammonia (NH 3 ) 48, Nitrogen oxides (NOx), Sulfur dioxide (SO 2 ), and Volatile organic compounds (VOC). HAPs, also known as toxic air pollutants, are those pollutants that pose high risk to human health. Emissions are presented for each of the 188 HAPs. HAPs include benzene, chlorine, lindane, methanol, naphthalene, parathion, phenol, toluene, and a number of metal compounds, including lead. 49 State-provided databases represent the primary source of point source air emissions data used in the inventory. State and local agencies and tribes supplied HAP and criteria emission inventory data to the EPA s Emission Inventory and Analysis Group (EIAG) which compiles the NEI. EIAG complemented these data with data and facility lists from the EPA s Emission Standards Division for Maximum Achievable Control Technology (MACT) and Section 112(k) Area Source Standards categories, and emission inventory data for electric generating units from the Department of Energy s Energy Information Agency and EPA s Clean Air Markets Division. Finally, EIAG also used data from the Toxics Release Inventory (TRI) for HAPs to ensure that emissions data for facilities that report to TRI are included in the NEI. The following sections describe the compilation of NEI data for use in TEAM for each of the three categories of emissions: point (Section 4.2.1), area (Section 4.2.2), and mobile (Section 4.2.3) Point Source Air Emissions The scope of the point source inventory covers major sources, defined in the CAA as sources that have the potential to emit 10 tons per year or more of one HAP, or 25 tons per year or more of any combination of HAPs. Smaller point sources with annual emissions below these thresholds are covered in the inventory as CAPs defined under the Clean Air Act of 1970 also include ground level ozone and lead. Ozone, however, is not emitted directly; it forms by chemical reactions of organic compounds with nitrogen oxides in the air, mediated by sunlight. Lead is both a criteria air pollutant and a hazardous air pollutant, and EPA tracks emissions of lead as a hazardous air pollutant. (for more information, see Ammonia reacts with nitric and sulfuric acids in the atmosphere to form fine particulate matter, so EPA tracks ammonia emissions. See for a list of hazardous air pollutants and their chemical abstract service (CAS) numbers. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 44

52 area sources (Section 4.2.2). Because the NEI data are designed to support air quality modeling, the dataset includes facility-specific descriptive data such as the geographical coordinates, stack, emissions and process description. The NEI inventory classifies sources according to the NAICS framework, SIC codes, or MACT codes TEAM Data Specification and Preparation Since TEAM is configured to process economic and emission/resource use data by NAICS sector, the baseline emissions data need also to be in the NAICS framework. The 2002 NEI provides NAICS or SIC codes for the majority of reporting facilities. When only SIC codes are provided, we mapped the reported emissions to the appropriate NAICS code using the same concordance that was used for the water dischargers data (Section ). 50 However, the impact of SIC-NAICS emissions reassignment is relatively minimal. For HAP emissions, percent of the total emissions by tons are released by facilities with valid NAICS codes. Facilities that do not have NAICS information but have valid SIC codes instead account for 0.42 percent of total HAP emissions. For CAP emissions, percent of the total emissions by tons are released by facilities with valid NAICS codes. Facilities that do not have NAICS information but have valid SIC codes account for 0.08 percent of total CAP emissions. Table 4-6 presents the percentages of HAP and CAP emissions for facilities that reported valid NAICS or SIC codes. Table 4-6: Amount of Point Source Air Emissions by Reported Classification Codes Classification HAP CAP System Tons/Year Percent Tons/Year Percent NAICS 927, % 0 0% SIC 3, % 27,303, % Missing Codes 1, % 23, % TOTAL 932, ,327, As shown in Table 4-6, less than one percent of both HAP and criteria emissions are reported by facilities that did not provide a valid NAICS or SIC code. For those records that did not have a valid industry code, we used the following approach: First, we used the information on facility s name and address provided in the NEI to obtain Facility Registry System (FRS) unique codes from EPA s Facility Finder (OTIS on-line tool). 51 Second, using FRS codes, we obtained an industry classification code (NAICS or SIC) from EPA s Integrated Data for Enforcement Analysis (IDEA) system. FRS codes represent the industry classification assigned to the facility by various EPA programs that regulate the facility To convert emissions reported by SIC, we used a SIC-NAICS concordance file that was developed for the purpose of translating industry codes. When there was a one-to-one correspondence between NAICS and SIC codes, the assignment was straightforward. In cases when one SIC code mapped to multiple NAICS codes, the emissions attributed to the SIC code were distributed across corresponding NAICS codes in proportion to the level of economic activity associated with each of the NAICS industry in a particular state, as indicated in the TEAM Economic Baseline dataset. OTIS Facility Finder allows one to submit facility lists from external data sources, and pass those sources through a screening process to determine whether any matches appear in EPA's systems. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 45

53 By implementing the approach described above, we were able to recover additional NEI emissions records that did not originally have a valid industry classification code. As a result, an additional 6,593 tons/year of criteria emissions were captured by the air point emissions dataset compiled for use in TEAM. In the TEAM framework, point sources air emissions data are compiled at the facility level. Since NEI may include more than one emission source (e.g., several stacks) within the same facility, we aggregated emissions by individual chemical and 4-digit NAICS code for unique facilities within each state. The resulting TEAM baseline data file includes, for each point source air emissions observation, the TEAM media type (point source air emissions), facility identifier, state, 4-digit NAICS sector, chemical identifier (CAS number), annual amount discharged (in tons per year), year of data (2002), and the data source (NEI) Data Limitations The 2002 NEI is a composite of emission estimates generated by state and local regulatory agencies, tribes, industry, and EPA. EIAG, which developed the air emissions inventory, states that, because the estimates originated from a variety of sources and estimation methods and were developed for differing purposes, they vary in quality, pollutants included, level of detail, and geographic coverage. However, this compilation of emissions estimates represents the best available information to date. When processing NEI data for use in TEAM, we undertook several data manipulation techniques to validate state FIPS codes, NAICS industry codes, and pollutant names. Due to invalid entries for certain records, 0.1 percent and 1.4 percent of total emissions of HAP and CAP by tons reported in NEI were omitted, respectively. Table 4-7 compares amounts of HAP and CAP point sources air emissions in the original NEI database and the TEAM emissions file and notes the fraction of emissions omitted during the data compilation process. Table 4-7: Amount of Point Source Air Emissions Omitted During Data Compilation HAP CAP Tons/Year Percent Tons/Year Percent NEI 932, % 27,327, % TEAM 931, % 26,946, % Omitted % 380, % Area Source Criteria Air Emissions NEI database for area source emissions contains data on emissions not attributed to any particular entity, and is therefore different from the NEI point source database. Area source emissions are reported for individual pollutants at the county, state, and national levels using 10-digit Source Classification Codes (SCC). 53 The SCC framework is substantially different in concept from the NAICS framework as the SCC framework Each facility is tracked for compliance under three environmental statutes: the Clean Air Act (CAA), the Clean Water Act (CWA), and the Resource Conservation and Recovery Act (RCRA). As opposed to the NAICS/SIC framework used for point source emissions. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 46

54 classifies emission sources rather than industrial or economic processes. For the purpose of this analysis we used state-level emissions data Data Preparation and Configuration As mentioned above, for the purpose of compiling the TEAM emissions baseline data, we used state-level data, which are reported by NEI by 10-digit SCC code and pollutant. The original dataset obtained from EPA contains 90,401 records. 54 The first step of preparing the data for use in developing the TEAM baseline involved reviewing these records for any anomalies and/or inconsistencies, such as duplicate records, and selecting the parameters to be used in the TEAM baseline. For example, we retained only those data points that represent annual emissions (as compared to seasonal and daily emissions), and non-zero emissions. 55 Additionally, we removed emissions records for Puerto Rico and Virgin Islands since no corresponding economic activity is reported in the TEAM Economic baseline (these jurisdictions are excluded from the 2002 Economic Census). Of the various parameters used to report Particulate Matter (PM) emissions, we selected PM10-PRI (10 microns, filterable and condensable combined) and PM25-PRI (2.5 microns, filterable and condensable combined), so as not to double-count PM emissions in TEAM. 56 As a result of these adjustments, the master NEI dataset used to develop the TEAM emissions baseline contains 76,630 unique observations, which represent 67,783,741 tons of emissions of 224 pollutants in 515 SCC categories. Table 4-8 below provides information for the ten SCC codes most often reported, based on the number of observations. Table 4-8: SCC Codes with Highest Occurrence Frequency SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Frequency Percent of Total Records Waste Disposal, Treatment, and Wastewater Treatment Public Owned Total Processed 2, % Recovery Miscellaneous Area Sources Other Combustion Forest Wildfires Total 1, % Stationary Source Fuel Total: Boilers and IC Commercial/Institutional Distillate Oil Combustion Engines 1, % Industrial Processes Food and Kindred Products: Commercial Cooking Under-fired SIC 20 - Charbroiling Charbroiling 1, % Industrial Processes Food and Kindred Products: Commercial Cooking Conveyorized SIC 20 - Charbroiling Charbroiling 1, % Since some of these records provide duplicative information, we did not sum the corresponding tons of emissions contained in the original data set. Nine SCCs had only zero-emissions reported: Bituminous/Subbituminous Coal (Total: All Boiler Types); Agricultural Stack Burning (Total, all crop types); Anthracite Coal (Total: All Boiler Types); Bulk Terminals: All Evaporative Losses (Crude Oil); All Storage Types: Working Loss (Crude Oil); All Storage Types: Working Loss (Jet Naphtha); Marine Vessel (Tank Cleaning); Fertilizer Application (Other Straight Nitrogen); and Fertilizer Application (Ammonium Phosphates (see also subsets (-13, -14)). The omission of these sectors results in the removal of 140 observations from the NEI data set. We eliminated records for emissions reported as PM10-FIL and PM25-FIL (filterable portion only for PM10 and PM25), PM-CON (condensable portion only), and PM-PRI (primary PM; includes filterable and condensable fractions for 10 and 2.5-micron particulates Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 47

55 Table 4-8: SCC Codes with Highest Occurrence Frequency SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Frequency Stationary Source Fuel Commercial/Institutional Combustion Stationary Source Fuel Industrial Combustion Stationary Source Fuel Residential Combustion Waste Disposal, Treatment, and Open Burning Recovery Stationary Source Fuel Residential Combustion Kerosene Distillate Oil Distillate Oil Residential Kerosene Total: All Combustor Types Total: Boilers and IC Engines Total: All Combustor Types Household Waste (use xxx for Yard Wastes Total: All Heater Types Percent of Total Records 1, % 1, % 1, % 1, % 1, % Since TEAM is configured to process economic and emission/resource use data by NAICS sector, the baseline emissions data also have to be in the NAICS framework. Consequently, the second step in preparing the NEI area emissions data for use in TEAM involved mapping SCCs into corresponding 4-digit NAICS codes and adjusting emissions accordingly. We followed the approach used to develop the prior TEAM emission baseline dataset for area source emissions. 57 We mapped the 515 SCC categories represented in the interim dataset to the corresponding NAICS sectors using the following methodology: For most SCC categories corresponding SIC codes are provided within the 2002 nonpoint source NEI database. We used this information in combination with the SCC-SIC mapping suggested by E.H. Pechan (which was used in the previous data development for TEAM). We then used the SIC- NAICS mapping, developed by the Economic Census. 58 Because certain SCC categories are very broad and span across a number of industries, some SCC categories map to a large number of NAICS sectors (a maximum of 286 NAICS mapped to one SCC). Once the sector mapping was determined, we developed a numeric concordance to assign emissions reported by SCC categories to the appropriate four-digit NAICS codes using shares calculated based on TEAM s 2002 state-level economic baseline (developed from the 2002 Economic Census). 59 Table 4-9 below illustrates the concept used to develop the SCC-NAICS concordance. In cases where more than one NAICS sector corresponds to one SCC category, emissions are allocated across four-digit NAICS codes in proportion to the revenue reported for each of the NAICS sectors at the state level. In the example shown in Table 4-9, shares assigned to the corresponding NAICS sectors differ between the two states. This approach enables us to allocate emissions reported by SCC category only to those NAICS industry sectors for which economic activity is reported within a given state. This ensures that we capture the maximum amount of emissions since emissions not associated with economic activity would be excluded by the TEAM model For details, refer to Trade and Environmental Assessment Model: Model Description, Abt Associates, April 14, For a list of SIC codes and corresponding NAICS sectors, see For a discussion of the state-level economic baseline dataset, refer to Chapter 3. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 48

56 Table 4-9: Example of the SCC-NAICS Concordance State SCC Code Alabama Alaska SCC Level I Mobile Sources Mobile Sources SCC Level II Unpaved Roads Unpaved Roads SCC Level III SCC Level IV Industrial Total: Unpaved Roads Fugitives Industrial Total: Unpaved Roads Fugitives NAICS Code NAICS Name Share General Freight Trucking Specialized Freight Trucking General Freight Trucking Specialized Freight Trucking 77% 23% 56% 44% Certain SCC categories are defined broadly and map to a very large number of NAICS codes (e.g., up to 286 different NAICS sectors at the 4-digit level). Appendix C provides more detail regarding these SCC categories. The emissions mapping/apportioning process outlined above initially left out certain emissions reported in NEI for the following reasons: (1) NAICS sectors corresponding to the reported SCC categories are entirely outside the scope of the Economic Census or (2) NAICS sectors have no economic activity reported in the state where the emissions are reported, based on the TEAM economic baseline. Because of the magnitude of emissions represented by household activities, we elected to keep these emissions in the TEAM baseline dataset even though the TEAM modeling framework does not presently allow for these emissions to be analyzed. 60 As a result, 14,688 observations, representing 30 SCC categories, 139 pollutants 61 and 6,850,942 tons of emissions or approximately 10 percent of total emissions in the master dataset were assigned the NAICS code HHHH to represent the household sector. More detailed information regarding these emissions is provided in Appendix C. SCC categories that were eliminated entirely during the mapping/apportioning process include: categories related to rail transportation, fire management activities for intentional burning (e.g., prescribed burning for forest management, prescribed burning for rangeland) and accidental fire (e.g., forest wildfires, motor vehicle fires), etc. Records in the NEI master file that are excluded from the TEAM baseline account for 5,690 observations and 23,914,091 tons of emissions (Appendix C). Over 97% of NEI emissions (23,137,016 tons) excluded from the TEAM baseline are attributed to Forest Wildfires and Prescribed Burning for Forest Management, two SCC categories that have no corresponding NAICS sector in the TEAM Economic Baseline. Additionally, the exclusion of these and other NEI emissions from the TEAM baseline results in the complete exclusion of 7 pollutants (Table 4-10), out of a total of 224 pollutants. Data in the final dataset for use in TEAM are provided by state, NAICS industry category, and pollutant. The final TEAM baseline dataset contains 516,720 non-point emissions records that represent 287 NAICS While the Economic Census does not report economic activity for the household sector, including these emissions in the baseline dataset may allow for the eventual evaluation of the household sector, for example using household income data available from other Census data sources. 23 pollutants reported in NEI are unique to the household sector. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 49

57 industry sectors (including sector HHHH which represents household activities), 217 pollutants, and 43,869,650 tons of emissions (or 65% of emissions reported in the master NEI dataset, by tons). Table 4-10: Pollutants Eliminated Entirely Pollutant Frequency m-cresol 1 Benzo(c)phenanthrene 108 Benzo(a)fluoranthene Methylpyrene 108 Hydrazine 1 Methylchrysene 108 Methylbenzopyrene Limitations Key limitations of the nonpoint emissions data incorporated in TEAM are summarized below. Since nonpoint NEI data serve as a basis for the nonpoint emissions database used in TEAM, the TEAM baseline dataset inherits uncertainties and limitations of the original NEI data. For instance, when emissions data were not reported for 2002, EPA brought forward data from the previous data release in Nevertheless, according to NEI, these data represent the best nonpoint source emissions estimates available. 62 The development of the concordance to translate emissions values provided in one classification framework to another introduces additional uncertainty due to the following assumptions: First, we assume that each sector generates the amount of emissions that is directly proportional to level of economic activity in this sector relative to the total economic activity in a given state. Second, due to broad SCC code definitions and a significant difference in concepts underlying SCC and NAICS classification frameworks, when assigning appropriate NAICS codes to SCC categories, we made assumptions as to the nature of the process or activity that generates the emissions, and the extent to which such process or activity is captured by the economic activity. Emissions may potentially be assigned to economic sectors that did not, in fact, generate these emissions. Conversely, emissions may not be assigned to economic sectors that did generate these emissions For more information of nonpoint NEI data collection and estimation methodology, see ftp://ftp.epa.gov/emisinventory/2002finalnei/documentation/nonpoint/2002nei_final_nonpoint_documentation0206 version.pdf An alternative approach would have involved improving the SCC-NAICS map for combustion-related SCC emissions for the manufacturing sector based on data from the Department of Energy s Manufacturing Energy Consumption Survey (MECS). The survey provides information on energy use by energy source type and NAICS codes at various NAICS level and could be used to refine the weights assigned to different NAICS sectors. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 50

58 4.2.3 Mobile Source Air Emissions For the purpose of aligning emissions data with the 2002 Economic Census data, we used the final version (version 3) of the 2002 NEI database, which was released in September The NEI mobile source emissions database, like the NEI nonpoint source emissions database, contains data on emissions not attributed to any particular entity, and is therefore different from the NEI point source database. Mobile source emissions are reported at the county level within the 10-digit Source Classification Code (SCC) framework by pollutant. 65 For the purpose of developing the TEAM baseline data, we used state-level emissions data Data Preparation and Configuration The NEI mobile CAP and HAP data include both highway and off-highway emission sources. Highway emissions are attributed to vehicles that may be used in highway transportation, such as trucks, busses, and motorcycles. Off-highway emissions are attributed to equipment that needs fuel to operate and is not suitable for highway use, such as aircraft, marine vessels, and agricultural and construction equipment. We processed each set of emissions separately and aggregated the final data into the TEAM baseline dataset. Table 4-11 lists the categories of highway and off-highway vehicles captured by the mobile emission inventory, indicating the emission source and fuel type, when applicable; these are the main vehicle type-fuel type combinations covered by the SCC categories in the original NEI mobile emissions data For highway emissions, we used a revised set of data, corrected to remove duplicate records discovered in the September 2007 data set. As opposed to the NAICS/SIC framework used for point source emissions. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 51

59 Table 4-11: Vehicles and Corresponding Fuel Type Vehicle Classification Fuel Type 1 Off-Highway Emission Group Agricultural Equipment Gasoline, 2-stroke Gasoline, 4-stroke LPG CNG Diesel Aircraft -- Airport Ground Support Equipment Commercial Equipment Commercial Marine Vessels Pleasure Aircraft Railroad Equipment Construction and Mining Equipment Underground Mining Equipment Industrial Equipment Lawn and Garden Equipment Logging Equipment CNG LPG Diesel Gasoline: 4-Stroke LPG CNG Diesel Gasoline: 2-Stroke Gasoline: 4-Stroke Diesel Residual Gasoline Gasoline 2-Stroke Gasoline 4-Stroke Diesel Diesel Gasoline 2-Stroke LPG CNG LPG Diesel Gasoline: 2-Stroke Gasoline: 4-Stroke Diesel CNG LPG Diesel Gasoline: 2-Stroke Gasoline: 4-Stroke LPG Diesel Gasoline: 2-Stroke Gasoline: 4-Stroke Diesel Gasoline: 2-Stroke Gasoline: 4-Stroke Highway Emission Group Light Duty Gasoline Vehicle (LDGV) Gasoline Light Duty Gasoline Trucks 1 & 2 (LDGT) Gasoline Heavy Duty Gasoline Vehicle (HDGV) Gasoline Motorcycles Gasoline Light Duty Diesel Vehicle (LDDV) Diesel Light Duty Diesel Trucks (LDDT) Diesel Heavy Duty Diesel Vehicle (HDDV) Diesel Notes: 1 LPG Liquefied Petroleum Gas; CNG Compressed Natural Gas Off-Highway Emissions As mentioned in Section 4.2.3, mobile source emissions are reported at the county level by 10-digit SCC code and pollutant. For the purpose of compiling the TEAM emissions baseline data, we aggregated county- Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 52

60 level emissions to the state. The original off-highway NEI dataset contains 29,309,787 county-level observations, which collapse to 539,774 state-level observations accounting for 32,164,442 tons of emissions of 93 pollutants in 257 SCC categories. 66 In this first step of data preparation for the TEAM baseline, we reviewed state-level off-highway NEI dataset for any anomalous observations and/or inconsistencies, such as records with national-level emissions present among state-level records. We made the following adjustments to these data: First, we removed eight observations with national-level emissions (1,639 tons of emissions); all of these emissions were reported for one SCC category ( : Mobile Sources/Railroad Equipment/Diesel/Line Haul Locomotives: Class I Operations). Note, that the original off-highway dataset contains state-level emissions for this SCC category as well; consequently, removing these anomalous observations with national-level emissions did not result in losing pollutants pertinent to this SCC category. Second, to align mobile emissions with the economic activity data, we removed 17,418 emissions records for Puerto Rico and Virgin Islands (311,120 tons of emissions) since no corresponding economic activity is reported in the TEAM Economic baseline for these jurisdictions (these jurisdictions are excluded from the 2002 Economic Census). Third, we adjusted the Particulate Matter (PM) emission category. PM is reported in NEI according to particulate size (2.5 microns (PM25), 10 microns (PM10), or as total (PM) or particulate fractions (filterable (FIL), condensable (CON), or filterable and condensable combined (PRI)). To avoid double-counting of PM emissions while also providing a comprehensive dataset, we retained PM10- PRI and PM25-PRI emission records. We eliminated 99 records of emissions (40,278 tons of emissions) reported as PM10-FIL and PM25-FIL (filterable portion only for PM10 and PM25), PM- CON (condensable portion only), and PM-PRI (primary PM; includes filterable and condensable fractions for 10 and 2.5-micron particulates). As a result of these adjustments, the master off-highway NEI dataset used to develop the TEAM emissions baseline contains 522,249 unique observations, which represent 31,811,404 tons of emissions of 90 pollutants in 257 SCC categories (99 percent of emissions reported in the original off-highway NEI dataset, by tons). 67 Table 4-12 below provides information for the ten SCC categories with the highest levels of emissions, based on the total off-highway emissions contained in the master off-highway dataset There were no duplicate records in the original NEI dataset. None of the SCC categories and/or pollutants was eliminated entirely as the result of adjustments to the original offhighway NEI data, except for pollutants eliminated as the result of adjustments to PM emission category. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 53

61 Table 4-12: SCC Categories with Highest Levels of Off-Highway Emissions SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Mobile Sources Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Lawn and Garden Equipment Lawn and Garden Equipment Turf Equipment (Commercial) Lawn and Garden Tractors (Residential) Emissions (Tons) Percent of Total Emissions 2,758, % 2,324, % Mobile Sources Pleasure Craft Gasoline 2-Stroke Outboard 2,188, % Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Commercial Equipment Mobile Sources Railroad Equipment Diesel Mobile Sources Liquefied Petroleum Gas (LPG) Industrial Equipment Generator Sets 1,822, % Line Haul Locomotives: Class I Operations 1,234, % Forklifts 1,142, % Mobile Sources Pleasure Craft Gasoline 4-Stroke Inboard/Sterndrive 1,022, % Mobile Sources Mobile Sources Mobile Sources Off-highway Vehicle Diesel Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Agricultural Equipment Lawn and Garden Equipment Lawn and Garden Equipment Agricultural Tractors 928, % Lawn and Garden Tractors (Commercial) Lawn Mowers (Residential) 867, % 849, % Highway Emissions We aggregated county-level highway emissions across states. The original highway NEI dataset contains 14,094,378 county-level observations, which collapse to 347,604 state-level observations accounting for 75,822,101 tons of emissions of 61 pollutants in 636 SCC categories. 68 In order to prepare highway emission data for development of the TEAM baseline, we reviewed the state-level highway NEI dataset for any anomalous observations and/or inconsistencies. As a result of this review, we removed emissions reported for Puerto Rico and Virgin Islands (11,844 observations accounting for 580,498 tons emissions). 69 Table 4-13 below provides information for the ten SCC codes with the highest levels of emissions, based on the total highway emissions contained in the master highway dataset Just like in case of off-highway emissions, there were no duplicate records in the original highway NEI dataset. Unlike off-highway NEI dataset, highway NEI dataset did not contain observations for either PM10-FIL, PM25- FIL, PM-CON, or PM-PRI. Further, there were no observations with zero emissions. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 54

62 Table 4-13: SCC Categories with Highest Levels of Highway Emissions SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV X X X X X X X X X X Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Highway Vehicles - Gasoline Highway Vehicles - Gasoline Highway Vehicles - Gasoline Highway Vehicles - Gasoline Highway Vehicles - Gasoline Highway Vehicles - Gasoline Highway Vehicles - Gasoline Highway Vehicles - Gasoline Highway Vehicles - Gasoline Highway Vehicles - Gasoline Light Duty Gasoline Vehicles (LDGV) Light Duty Gasoline Vehicles (LDGV) Light Duty Gasoline Vehicles (LDGV) Light Duty Gasoline Vehicles (LDGV) Light Duty Gasoline Vehicles (LDGV) Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5) Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5) Light Duty Gasoline Vehicles (LDGV) Light Duty Gasoline Vehicles (LDGV) Light Duty Gasoline Trucks 1 & 2 (M6) = LDGT1 (M5) Urban Interstate: Exhaust Urban Other Principal Arterial: Exhaust Rural Interstate: Exhaust Urban Minor Arterial: Exhaust Rural Other Principal Arterial: Exhaust Urban Interstate: Exhaust Urban Other Principal Arterial: Exhaust Rural Major Collector: Exhaust Urban Local: Exhaust Rural Interstate: Exhaust Emissions (Tons) Percent of Total Emissions 4,801, % 4,722, % 3,904, % 3,831, % 3,342, % 2,832, % 2,794, % 2,546, % 2,402, % 2,356, % As a result of this adjustment, the master highway NEI dataset used to develop the TEAM emissions baseline contains 335,760 unique observations, which represent 75,241,602 tons of emissions of 61 pollutants in 636 SCC categories (99 percent of emissions reported in the original highway NEI dataset, by tons) Data Configuration Since TEAM is configured to process economic and emission/resource use data by NAICS sector, the baseline emissions data also have to be in the NAICS framework. Consequently, we mapped SCC codes onto corresponding 4-digit NAICS codes and apportioned emissions accordingly, following a different approach for each emission group. 70 Just as it was the case with off-highway emissions, none of the SCC categories and/or pollutants (except for adjustments to PM emissions category) were eliminated entirely as the result of adjustments to the state-level NEI data. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 55

63 Off-Highway Emissions In order to map SCC codes for off-highway emissions into corresponding 4-digit NAICS sectors, we followed the approach used to develop the prior TEAM emission baseline dataset for mobile source emissions. 71 We mapped the 229 SCC categories represented in the master off-highway dataset to digit NAICS codes that could most reasonably be linked to each SCC category. 72 The numeric concordance used to assign emissions reported by SCC categories to the appropriate four-digit NAICS codes is based on the TEAM s 2002 state-level economic baseline (developed from the 2002 Economic Census). 73 Table 4-14 below illustrates the concept used to develop the SCC-NAICS concordance. In cases where more than one NAICS sector corresponds to one SCC category, emissions are allocated across four-digit NAICS codes in proportion to the revenue reported for each of the NAICS sectors at the state level. In the example shown in Table 4-14, shares assigned to the corresponding NAICS sectors differ between the two states. This approach enables us to allocate emissions reported by SCC category only to those NAICS industry sectors for which economic activity is reported within a given state. This ensures that we capture the maximum amount of emissions since emissions assigned to sectors that have no reported economic activity would be excluded in TEAM calculations. Table 4-14: Example of the SCC-NAICS Concordance State SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV NAICS Code NAICS Name Share Alabama Mobile Sources Off-highway Vehicle Recreational Gasoline, 2- Equipment Stroke All Terrain Vehicles Amusement Parks and Arcades Other Amusement and Recreation Industries 8% 92% Alaska Mobile Sources Off-highway Vehicle Recreational Gasoline, 2- Equipment Stroke All Terrain Vehicles Amusement Parks and Arcades Other Amusement and Recreation Industries Scenic and Sightseeing Transportation, Land 3% 64% 33% Certain SCC categories are defined broadly and often map to a very large number of NAICS codes (e.g., up to 156 different NAICS sectors at the 4-digit level). Appendix C provides more detailed information on these SCC categories. The emissions mapping/apportioning process outlined above initially left out certain emissions reported in NEI for the following reasons: (1) NAICS sectors corresponding to the reported SCC categories are entirely outside the scope of the Economic Census or (2) NAICS sectors have no economic activity reported in the state where the emissions are reported, based on the TEAM economic baseline For details, refer to Trade and Environmental Assessment Model: Model Description, Abt Associates, April 14, This number includes HHHH NAICS code we created for allocation of residential emissions, which is not present in Economic Census. For a discussion of the state-level economic baseline dataset, refer to Chapter 3. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 56

64 Because of the magnitude of emissions attributed to household/private sector, we elected to keep these emissions in the TEAM baseline dataset even though the TEAM modeling framework does not presently allow for these emissions to be analyzed. 74 As a result, 3,891,103 tons of emissions of 87 pollutants represented by 13 SCC categories 75 or approximately 13 percent of total off-highway emissions in the master dataset were assigned to the pseudo NAICS code HHHH to represent the household/private sector. Appendix C provides more detailed information on emissions associated with the household/residential off-highway vehicles. Observations in the NEI master off-highway file that are excluded from the TEAM baseline account for 2,215,400 tons of emissions. Almost 90 percent of NEI emissions (2 million tons) excluded from the TEAM baseline are attributed to Railroad Equipment and Pleasure Crafts, two SCC categories that have no corresponding NAICS sector in the TEAM Economic Baseline. The exclusion of these and other NEI emissions from the TEAM baseline results in the complete exclusion of 3 pollutants (Table 4-15), out of a total of 90 pollutants. Table 4-15: Pollutants Eliminated Entirely from the Master Off-Highway Emissions Dataset Pollutant Code Pollutant Name Emissions (Tons) 109 Beryllium & Compounds Cadmium & Compounds Mercury & Compounds Data in the final off-highway dataset are provided by state, NAICS industry category, and pollutant. The dataset contains 690,406 off-highway emissions records that represent 254 NAICS industry sectors, 87 pollutants, and 29,596,004 tons of emissions (or 93 percent of emissions reported in the master off-highway NEI dataset, by tons). These counts include emissions generated by the household/private sector. Highway Emissions In order to map SCC codes onto corresponding 4-digit NAICS codes for highway emissions, we used the following methodology, building on the approach used in developing the prior TEAM baseline dataset, updated to reflect more recent data: 76 We apportioned emission estimates among public, household/private, and commercial activities based on estimated vehicle-mile-traveled (VMT) at the state level, and further apportioned the commercial activities to 4-digit NAICS codes based on the U.S. Census Bureau s Vehicle Inventory and Use Survey (VIUS). VIUS is conducted every five years as part of the Economic Census. The 2002 survey provides data on the physical While the Economic Census does not report economic activity for the household sector, including these emissions in the baseline dataset may allow for the eventual evaluation of the household sector, for example using household income data available from other Census data sources. 23 pollutants reported in NEI are unique to the household sector. For details, refer to Trade and Environmental Assessment Model: Model Description, Abt Associates, April 14, Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 57

65 and operational characteristics of the U.S. truck population based on a probabilistic sampling of household/private and commercial trucks registered or licensed in the United States as of July 1, The first step in the mapping process involves estimating vehicle-miles-traveled within a state for commercial, public, and household/private uses for the seven types of vehicles used in the NEI for criteria air pollutants. NEI classifies highway mobile criteria emissions into seven categories based on the type of vehicle: motorcycles, light duty gas vehicles (LDGV), light duty diesel vehicles (LDDV), light duty gas trucks (LDGT), light duty diesel trucks (LDDT), heavy duty gas trucks (HDGT), and heavy duty diesel trucks (LDDT). More information regarding the VMT distribution is provided in Appendix C. Using vehicle registration data from the Highway Statistics 2002, published by the Federal Highway Administration of the U.S. Department of Transportation (FHWA), we grouped registered vehicles by public and a combination of commercial and household/private uses. Because we lacked additional information regarding the relative use of public and commercial/household/private vehicles, we assumed that within a given NEI vehicle type, the VMT per vehicle are the same for the two classes of uses. 78 To estimate state-level VMT, we then used the state-level numbers of public and commercial/household/private vehicles for the seven NEI vehicle types, and multiplied it by the national average VMT per vehicle and type provided by FHWA. For each NEI vehicle type, we then used VIUS data to allocate the commercial, household, and private VMT across commercial and household/private use categories. The state-level VMT allocation factors by vehicle type among commercial, public, and household/private uses is summarized in Appendix C. The apportionment process is complicated by the fact that the HPMS and VIUS databases use different vehicle classifications than those used in NEI. Table 4-16 shows how we reconciled the HPMS and VIUS categories with the NEI classification system. After this step, we had three sets of highway emissions: household/private, public, and commercial. Public and household/private sectors account for 1,503,651 and 38,457,055 tons of emissions, respectively, of 61 pollutants in 636 SCC each. Commercial sector accounts for 35,280,896 tons of emissions of 61 pollutants in 468 SCC. Note, that motorcycles (MC) and passenger vehicles (LDGV and LDDV) are not a part of the commercial sector. Consequently, commercial emissions do not include 168 SCC categories related to either motorcycles (35,935,853 tons of emissions), LDGV (51,931 tons of emissions), or LDDV (376 tons of emissions) The survey was first conducted in 1963 and was discontinued after the 2002 survey year was processed. It had been conducted for years ending in "2" and "7." Reported data are for vehicle activity during the census calendar year. The survey excludes vehicles owned by federal, state, or local governments; ambulances; buses; motor homes; farm tractors; unpowered trailer units; and trucks reported to have been sold, junked, or wrecked prior to January 1 of the survey year. For more information on 2002 VIUS survey, refer to More information on Highway Statistics 2002 can be found at Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 58

66 The second step in the mapping process consists of estimating commercial VMT by 4-digit NAICS sector in each state. We used the VIUS data to generate the factors used to allocate state-level commercial uses across 14 VIUS groups (Table 4-17). We calculated different sets of allocation factors for heavy duty (HDGV and HDDV) and light duty (LDGT and LDDT) vehicles. Heavy duty vehicles account for 7,952,511 tons of highway emissions of 61 pollutants in 300 SCC categories. Light duty vehicles account for 27,328,385 tons of highway emissions in 61 pollutants in 168 SCC categories. After this last step, public and household/private highway emissions are still reported by SCC category, while commercial emissions are reported by VIUS group. To allocate commercial emissions across 4-digit NAICS codes, we first assigned 4-digit NAICS codes that could most reasonably be linked to each VIUS group and for which economic activity is reported in 2002 Economic Census. We then applied allocation factors calculated based on TEAM s 2002 state-level economic baseline (developed from the 2002 Economic Census) accounting only for economic activity reported for NAICS codes corresponding to the 14 VIUS groups (Table 4-17). Since the Economic Census does not provide economic activity for either the public or household/private sector, we assigned all household/private and public emissions to pseudo NAICS codes HHHH and PPPP, respectively. 79 Data in the final highway dataset are provided by state, 4-digit NAICS sector, and pollutant. This dataset contains 512,775 highway emission records that represent 255 NAICS industry sectors, 61 pollutants, and 75,241,602 tons of highway emissions, including emissions for public ( PPPP ) and household/private ( HHHH ) sectors (or 100 percent of emissions reported in the master highway NEI dataset, by tons). Public emissions account for 2 percent, household/private emissions account for 51 percent, and commercial emissions account for 47 percent of total final highway emissions. 79 As in the case of off-highway emissions attributed to the household/residential sector, because of the magnitude of highway emissions represented by household/private and public sectors, we decided to keep these emissions in the TEAM baseline dataset, even though the TEAM modeling framework does not presently allow for these emissions to be analyzed. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 59

67 Table 4-16: HPMS, VIUS and NEI Vehicle Classifications 80 HPMS VIUS NEI Motorcycles -- MC Passenger Car % LDGV % LDDV Other 2-Axle, 4- Tire Other 2-Axle, 4- Tire Single 1 (Pickups, Minivans, vans, SUV body types) 64.39% LDGT LDGT % LDDT Buses % HDGV % 95.32% HDGV HDDV Singe Unit Truck 2- Axle 6 Tire 2 Axle 6 Tire or more trucks Single 2 (excluding SUV, Pickup, Minivan, Van, SUV body type) 95.32% HDGV % HDDV Single Unit Truck 3 Axle Single Unit Truck 4 or More Axle Single Trailer: 4 or fewer Axle Combination Trucks Single 3 (excluding SUV, Pickup, Minivan, Van, SUV body type) Single 4 (excluding SUV, Pickup, Minivan, Van, SUV body type) Straight 1 + Straight 2 + Straight 18 + Tractor 1 + Tractor 2 + Tractor 44 (excluding SUV, Pickup, Minivan, Van, SUV body type) 9.14% HDGV % HDDV 2.63% HDGV % HDDV 100% HDDV Note: VIUS covers only trucks. The EPA vehicle classification is based on EPA s mobile vehicle categories. 80 This vehicle concordance was obtained from the final report Use of Locality-Specific Transportation Data for the Development of Mobile Source Emission Inventories published in September, 1996 for EPA (Volume IV, Chapter 2); it was used to develop the mobile emissions baseline for TEAM previously and has not been updated. This report, and consequently this vehicle concordance, has not been updated since then. Note, that in 2002 mobile NEI database, Single Unit Truck 4 or More Axle category is broken into Single Unit Truck 4-Axle and Single Unit Truck 5 or More Axle. We used HDGV and HDDV allocation factors reported for Truck 4 or More Axle in the 1996 report for our analyses of 2002 data (2.63% and 97.37% respectively). This report can be found at Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 60

68 Table 4-17: VIUS Industry Groups Industry Group Industry Name 01 For-hire transportation or warehousing 02 Vehicle leasing or rental 03 Agriculture, forestry, fishing, or hunting 04 Mining 05 Utilities 06 Construction 07 Manufacturing 08 Wholesale trade 09 Retail trade 10 Information services 11 Waste management, landscaping, or administration/support services 12 Arts, entertainment, or recreation services 13 Accommodation or food services 14 Other services Final TEAM Baseline Mobile Air Emissions Once both compiled, we combined the highway and off-highway emissions datasets to create the final TEAM total mobile emissions baseline. Final mobile emissions data for use in TEAM are provided by state, 4-digit NAICS industry category, and pollutant. This dataset contains 773,049 mobile emissions records that represent 288 NAICS industry sectors (including sectors HHHH and PPPP which represent household/private activities and activities by the public sector, respectively), 87 pollutants, and 100,946,503 tons of emissions (or 94 percent of emissions reported in the original NEI highway and off-highway emissions datasets, by tons). Household/private and public sectors account for 38 percent and 1 percent of mobile emissions, respectively. Table 4-18 and Table 4-19, below, provide information for the ten NAICS industry sectors and ten pollutants with the highest levels of emissions, respectively, based on the final total mobile emissions; these counts exclude emissions attributed to household/private and/or public sectors. Table 4-18: NAICS Industry Sectors with Highest Levels of Emissions NAICS Description Emissions (Tons) Percent of Total Emissions 5617 Services to Buildings and Dwellings 6,964, % 4872 Scenic and Sightseeing Transportation, Water 3,844, % 2362 Nonresidential Building Construction 2,773, % 2361 Residential Building Construction 2,768, % 2382 Building Equipment Contractors 2,395, % 7139 Other Amusement and Recreation Industries 2,141, % 6221 General Medical and Surgical Hospitals 1,710, % 1111 Oilseed and Grain Farming 1,372, % 4831 Deep Sea, Coastal, and Great Lakes Water Transportation 1,267, % 2211 Electric Power Generation, Transmission and Distribution 1,256, % Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 61

69 Table 4-19: Pollutants with Highest Levels of Emissions Pollutant Code Pollutant Name Emissions (Tons) Percent of Total Emissions CO Carbon Monoxide 46,169, % NOX Nitrogen Oxides 8,809, % VOC Volatile Organic Compounds 4,736, % SO2 Sulfur Dioxide 560, % PM10-PRI Primary PM10 (Filterables + Condensibles) 424, % Toluene 386, % PM25-PRI Primary PM2.5 (Filterables + Condensibles) 375, % Xylenes (Mixture of o, m, and p Isomers) 268, % ,2,4-Trimethylpentane 157, % Benzene 132, % Data Limitations Since mobile NEI data serve as a basis for the mobile emissions database used in TEAM, the TEAM baseline inherits uncertainties and limitations of the original mobile NEI data. Nevertheless, according to NEI, these data represent the best mobile source emissions estimates available. 81 The development of the concordance to translate emission values provided in one classification framework to another introduces additional uncertainty due to the following assumptions: First, we assume that each sector generates an amount of emissions that is directly proportional to the dollar value of economic activity in this sector relative to the total economic activity in a given state. In addition, when allocating commercial highway emissions reported by SCC across VIUS groups, we assume that the amount of emissions generated is directly proportional to the amount of VMT by heavy and light duty vehicles in a given VIUS group and state. Further, by using VIUS, which covers only trucks, we assumed that motorcycles and passenger cars (light duty gasoline and diesel vehicles) are not used for commercial purposes, and therefore, do not contribute to commercial emissions. Since commercial emissions are the only highway emissions that are mapped to valid NAICS categories present in the TEAM economic baseline at this time, emissions from motorcycles and passenger cars will not be taken into account in model results unless we find a way in the future to incorporate emissions from public and household/private uses into TEAM. Finally, uncertainty is introduced in mapping emissions reported in SCC to the relevant NAICS sector, given the significant difference in concepts underlying the two classification frameworks. In case of off-highway emissions, we made assumptions regarding the nature of the process or activity that generates the emissions and the extent to which such a process or activity reflect a categorized economic activity. In case of highway emissions, we made similar assumptions about VIUS groups when assigning NAICS industry categories to these 81 For more information of mobile NEI data collection and estimation methodology, see ftp://ftp.epa.gov/emisinventory/2002finalnei/documentation/mobile/2002_mobile_nei_version_3_report_ p df Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 62

70 groups. Emissions may potentially be assigned to economic sectors that did not, in fact, generate these emissions. Conversely, emissions may not be assigned to economic sectors that generated these emissions. In working with highway emissions, we relied on the HPMS-VIUS-NEI vehicle crosswalk from Consequently, our analysis is based on the assumption that the relationship between vehicle types across these three data sources remains the same as of Energy Use and Carbon Dioxide Emissions TEAM incorporates baseline data on energy use and the resulting carbon dioxide emissions (CO 2 ). The energy use and CO 2 emissions baseline files were developed based on 2002 energy use data to line up with the 2002 Economic Census and other TEAM baseline files. Key elements of the approach used in TEAM to incorporate energy use and CO 2 emissions include: TEAM employs two independent presentations of baseline energy use, by fuel type: National-level primary energy consumption, by fuel type, for 4-digit NAICS manufacturing sectors (e.g., NAICS 31-33) and mining sectors (NAICS 21); and State- and National-level primary energy consumption, by fuel type, for five aggregate economic sectors: residential, commercial, industrial, transportation, and electric power. 82 As explained below, these aggregations by economic sector and geography generally reflect the level at which data are provided in the underlying sources of energy consumption data. TEAM includes two presentations of baseline total CO 2 emissions, which have been estimated, respectively, from these two sets of energy consumption data: National CO 2 emissions for each 4-digit-level NAICS manufacturing sector and mining sector (so-called MECS/Census-derived data ); and Total CO 2 emissions by state, and nationally, for the five aggregate economic sectors (so called SEDS-derived data ). The baseline energy use and CO 2 emissions data are integrated into the TEAM framework so that economic changes from a trade or other economic event specified for 4-digit NAICS sectors are used to estimate changes in energy consumption and CO 2 emissions. This section describes the methodology used to compile the baseline data from primary sources of energy consumption data (Section 4.3.1), derive corresponding baseline CO 2 emissions (Section 4.3.2), and incorporate the information into TEAM (Section 4.3.3). Finally, Section describes the content of the TEAM baseline data files as it relates to energy use and carbon emissions Compilation of Energy Use Data The TEAM energy use baseline data set are derived from three primary sources of energy consumption data published by the Energy Information Administration (EIA) and the U.S. Census Bureau. They include: 82 The TEAM baseline also includes end-use energy consumption data where electric power energy use is allocated to end-users. Energy use in the transportation sector is allocated to value-added and non-value-added activities. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 63

71 2002 Manufacturing Energy Consumption Survey (MECS). Published by the DOE s Energy Information Administration (EIA), MECS reports energy consumption, by energy source, for manufacturing sectors (i.e., NAICS 31-33) nationally and for four Census Regions: Northeast, Midwest, South, and West. The data are not reported at a consistent NAICS level, but range from 3- digit NAICS to 6-digit NAICS, with increasing amounts of data suppression encountered as one progresses to more detailed sectors. MECS provides total energy use (fuel and non-fuel use), fueluse only, and non-fuel use data. Fuel types include: Total; Net Electricity; Residual Fuel Oil; Distillate Fuel; Natural Gas; LPM and NGL; Coal; Coke and Breeze; Other; and Shipments of Energy Sources Produced Onsite Economic Census Mining Industry Series. The U.S. Census publishes national-level data on selected inputs used by the mining sector (NAICS 21). The Census table, entitled Selected Supplies, Minerals Received for Preparation, Purchased Machinery, and Fuels Consumed by Type, provides the quantity and cost of purchased fuels (coal, distillate fuel oil, residual fuel oil, gas, gasoline, and other fuels) and the quantity of crude petroleum, natural gas, and coal produced and consumed at the same establishment for heat and power. Data are provided at the level of 6-digit NAICS sectors State Energy Data System (SEDS). EIA s SEDS reports energy consumption (by fuel type) at the state-level, but only for five highly aggregated energy consuming sectors: residential, commercial, industrial, transportation, and electric power. For each data source, energy use data was compiled by fuel at the appropriate level of geographical and sectoral resolution, i.e., national/4-digit NAICS for the MECS and Census data, and state/aggregated sectors for the SEDS data. The geographical and sectoral resolution used in the baseline data set reflect the most detailed level of resolution available in the primary data source. The methodology used in compiling TEAM data from each source is described in the following sections MECS As described above, MECS reports energy consumption for a variety of fuel categories for all 3-digit NAICS manufacturing sectors, some 4-digit NAICS manufacturing sectors (e.g. 3112, 3113, and 3114), and some 5- and 6-digit NAICS manufacturing sectors (e.g , , and ). Within the 2002 MECS national-level data set, thirteen sectors have data on energy consumption by fuel type reported directly at the 4-digit NAICS level. The data for these 4-digit NAICS codes are relatively complete across fuel categories. Nine of the 4-digit NAICS sectors, however, have only partial data reported some numeric values are replaced by qualifiers indicating that the value is less than the reporting threshold of 0.5 trillion BTUs (9 sectors) or the value was withheld to avoid disclosing data for individual establishments (one sector). An additional fourteen 4-digit NAICS sectors have data on energy consumption by fuel type reported for one or more 5- or 6-digit NAICS sub-sectors. These data can be summed to the relevant 4-digit NAICS sector, although the ensemble of reported 5-digit or 6-digit sectors is not always complete within any given 4-digit NAICS sector. The 5- and 6-digit NAICS sectors reported within each 4-digit NAICS sector are listed in Table Finally, in addition to sectors that are missing from the data set entirely, some of the 5- and 6-digit sectors have data suppressed for certain fuel categories, requiring the estimation of withheld values. Key steps of compiling the MECS data at the level of 4-digit NAICS for use in TEAM involved: Estimate withheld data. Some of the data points in the national-level MECS data, by sector and fuel category, are withheld for one of three possible reasons: Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 64

72 The energy consumption estimate is less than 0.5 trillion BTUs (the associated withholding code is * ); It is withheld to avoid disclosing data for individual establishments (the associated withholding code is W ); or, It is withheld because the relative standard error is greater than 50 percent (the associated withholding code is Q ). These data gaps were filled by: (1) assuming all values identified as less than 0.5 trillion BTUs are zero unless there was a residual to be apportioned from the reported total, 83 and (2) backing out withheld values based on the totals reported for the economic sector. When withheld values could not be determined at the 4-digit level based on the totals reported within the economic sector, the total remaining units were allocated among the withheld fuel types by using the relative proportion of energy consumption in the withheld fuel types for the parent 3-digit NAICS manufacturing sector. This allocation method assumes that the relative share of fuel consumed in the 4-digit NAICS sector for withheld fuel categories is similar to that of the 3-digit NAICS parent sector. More details on data adjustments made for each manufacturing sector are provided in Appendix C. Sum energy consumption across 5-digit and/or 6-digit NAICS sectors. When data were reported for sub-sectors only, the 4-digit NAICS value is calculated by summing the relevant 5-digit NAICS data (after filling in withheld values as described above if needed). Aggregate energy consumption across fuel and non-fuel uses. The total energy consumption of each fuel type is therefore calculating by summing the fuel and non-fuel use data from MECS. This total energy consumption can be used with the carbon emission factors to calculate CO 2 emissions (see Section 4.3.2), as the carbon emission factors represent blended carbon emission coefficients that account for the relative quantity of fuel and non-fuel uses associated with each energy source. When estimating energy consumption values for each 4-digit NAICS sector based on the MECS data set, only those values that had been suppressed in the original data source were filled in; Sectors not reported at either the 4-digit, 5-digit or 6-digit NAICS levels are assumed to have no fuel consumption. For example, MECS provide data for 4-digit sectors 3251, 3252, 3253, 3254, and 3259, but not 3255 and The reported 4-digit NAICS sectors represent 96 percent of the total fuel use energy consumption reported for 3- digit NAICS sector 325, implying that the remaining 4 percent may be found in sectors 3255 and Several other 4-digit NAICS sectors are similarly missing from the MECS (and TEAM) data. These sectors are instead included at the level of 3-digit NAICS only, reflecting the most detailed resolution at which information is provided in MECS: 313: Textile Mills; 314: Textile Product Mills; 315: Apparel; 316: Leather and Allied Products; 326: Plastics and Rubber Products; 332: Fabricated Metal Products; 333: Machinery; 335: Electrical Equipment, Appliances, and Components; 337: Furniture and Related Products; and 339: Miscellaneous. The existing TEAM Emissions Baseline does not apportion this residual among the two missing 4-digit NAICS sectors at the outset, but instead data at the level at which they were compiled (either 4-digit NAICS or 4-digit NAICS). The TEAM calculation framework, however, does distributes this residual among 83 In cases where we calculated an unaccounted for remainder in the total fuel consumption, we apportioned this remainder among the fuel categories in proportion to the relative fuel consumption at the 3-digit NAICS, rather than assume zero for withheld values. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 65

73 remaining 4-digit NAICS sectors during the analysis in proportion to their relative revenue as reported in the TEAM Economic Baseline. This is done in order to be consistent with how the interface presents the TEAM results (Section 8.3). As discussed below, the 3-digit NAICS residual not already accounted for at the level of 4-digit NAICS sectors is often a very small share of the total reported. For those sectors that are only reported at the level of 3-digit NAICS, it is recommended that the results be summed to that level to minimize the effects of the disaggregation process. Table 4-20 summarizes data coverage across 4-digit and 3-digit NAICS sectors. As shown in the table, coverage varies across sectors. While only 34 percent of the total fuel use energy consumption reported for sector 311 is reflected in the 4-digit NAICS TEAM baseline data, coverage is complete, or nearly so, for NAICS 312, 321, 322, 323, 324, 325, and 331. Overall, the TEAM baseline at the 4-digit NAICS level captures 79.5 percent of the total energy consumption reported in MECS within the 3-digit NAICS codes, as compared to 73.2 percent prior to filling in withheld data. 84 The remaining 20.5 percent of the total energy consumption is not accounted for at the 4-digit NAICS level but is only available at the level of 3-digit NAICS. Table 4-20: Comparison of Energy Consumption (Fuel-Use) for 4-digit NAICS and 3-digit NAICS. Σ(4-digit 4-digit Total Fuel- 3-digit Total Fuel- 4-digit NAICS Description NAICS) / NAICS Use (trillion NAICS Use (trillion (3-digit NAICS description) 3-digit Code BTUs) Code BTUs) NAICS 3111 Animal food Grain & oilseed milling Sugar & confectionery product mfg Fruit & vegetable preserving & specialty food Dairy product ,116 34% 3116 Animal slaughtering & processing Seafood product preparation & packaging Bakeries & tortilla Other food Beverages Tobacco % Textile Mills % Textile Product Mills % 3211 Sawmills & wood preservation Veneer, plywood, and engineered woods % 3219 Other Wood Products Pulp, paper, & paperboard mills 2, Converted paper products ,361 94% 3231 Printing and Related Support % 3241 Petroleum and Coal Products 3, , % 3251 Basic chemical mfg 2, ,769 94% 3252 Resin, syn rubber, & artificial syn fibers & filaments Pesticide, fertilizer, & other agricultural chemical mfg When added across fuel types, the total energy consumption within the 4-digit NAICS codes is 73.2 percent. When determined by the reported total energy consumption, the total energy consumption within the 4-digit NAICS codes is 79.5 percent. This difference exists because some data across fuel types is withheld to avoid disclosing data for individual establishments. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 66

74 Table 4-20: Comparison of Energy Consumption (Fuel-Use) for 4-digit NAICS and 3-digit NAICS. Σ(4-digit 4-digit Total Fuel- 3-digit Total Fuel- 4-digit NAICS Description NAICS) / NAICS Use (trillion NAICS Use (trillion (3-digit NAICS description) 3-digit Code BTUs) Code BTUs) NAICS 3254 Pharmaceuticals and medicines Paint, coating, and adhesive Soap, cleaning compound, and toilet preparation Other chemical product & preparation Clay product & refractory Glass & glass product Cement & concrete product ,052 73% 3274 Lime & gypsum product Other nonmetallic mineral product Iron & steel mills & ferroalloy 1, Steel products from purchased steel Alumina and Aluminum , % 3314 Nonferrous Metals, except Aluminum Foundries 157 Fabricated Metal Product % Machinery % 3341 Computer and peripheral equipment Communications equipment Audio and video equipment Semiconductor & other electronic component % 3345 Navigational, measuring, medical and control instruments Reproducing magnetic and optical media -- Electrical Equipment, Appliance and Component % 3361 Motor vehicle Motor vehicle body & trailer Motor vehicle parts Aerospace products & parts % 3365 Railroad rolling stock Ship & boat building Other transportation equipment -- Furniture and related product % Miscellaneous % Total 12,938 16, % U.S. Census Mining Series Data The 2002 Economic Census provides national-level data on purchased fuels by fuel type and 6-digit NAICS for the entire mining sector (NAICS 21). The quantities purchased are expressed in units that vary according to the fuel category. For the purpose of their inclusion in the TEAM baseline data set, these quantities are Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 67

75 converted into British Thermal Units (BTUs) using conversion factors from EPA and DOE, as summarized in The Climate Registry s General Reporting Protocol. 85 As with the MECS data, data provided for 6-digit NAICS codes are summed to their parent 4-digit NAICS code. In several cases (42 out of a total of 77 sector-fuel combinations), however, the Census files provide the dollar value of fuel purchases, but not the actual quantity consumed in order to avoid disclosing data for individual establishments. To fill in the withheld values, the fuel quantity was estimated based on the dollar value of fuel purchases using as basis the average implied price paid by other mining sectors for the same fuel. 86 More details on data adjustments made for each 4-digit NAICS by fuel type are provided in Appendix C SEDS As noted in the introduction to Section 4.3, SEDS reports energy consumption estimates for a variety of fuel categories at the state-level, for five aggregate economic sectors. The scope and approach used in compiling the TEAM baseline data for each of these five sectors is described below. Residential Sector. The SEDS residential sector consists of the numerous services dependent on energy use for household living. These include space heating and cooling, cooking, refrigeration, lighting and the powering of other appliances, among others. The residential sector excludes institutional living quarters. For completeness and possible future expansion of the framework, data for this sector are included in the TEAM baseline even though the TEAM framework does not include economic activity data (and therefore does not estimate emissions changes) for the residential / household sector. The sector is identified in TEAM by the pseudo NAICS code HHHH. Commercial Sector. The SEDS commercial sector consists of service-providing facilities and equipment of: businesses; Federal, State, and local governments; and other private and public organizations, such as religious, social, or fraternal groups. The commercial sector includes institutional living quarters. It also includes sewage treatment facilities General Reporting Protocol, May Available at GRP conversion factors are obtained from U.S. EPA, Inventory of Greenhouse Gas Emissions and Sinks: (2007), Annex 2.1, Tables A-31, A-32, A-35, and A-36, except: heat content factors for Unspecified Coal (by sector), Naphtha (<401 deg. F), and Other Oil (>401 deg. F) (from U.S. Energy Information Administration, Annual Energy Review 2006 (2007), Tables A-1 and A-5) and Carbon Content and Heat Content factors for Coke and LPG (from EPA Climate Leaders, Stationary Combustion Guidance (2007), Table B-1). A fraction oxidized value of 1.00 is from the Intergovernmental Panel on Climate Change (IPCC), Guidelines for National Greenhouse Gas Inventories (2006). Note: Default CO2 emission factors (per unit energy) are calculated as: Carbon Content Fraction Oxidized 44/12. Default CO2 emission factors (per unit mass or volume) are calculated using Equation 12d: Heat Content Carbon Content Fraction Oxidized 44/12 Conversion Factor (if applicable). Heat content factors are based on higher heating values (HHV). Since the implied price for a fuel category generally did not vary significantly across 6-digit NAICS sectors, we believe that this methodology provides reasonable estimates of quantities. One exception was the implied price for natural gas, which was much lower for sectors and than for the other mining sectors. Because of the large difference, we excluded these implied price values from the average implied oil price used in estimating quantity purchased by the other mining sectors. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 68

76 Industrial Sector. The SEDS industrial sector consists of all facilities and equipment used for producing, processing, or assembling goods. This sector encompasses the following types of activity: manufacturing (NAICS codes 31 33); agriculture, forestry, fishing and hunting (NAICS code 11); mining, including oil and gas extraction (NAICS code 21); and construction (NAICS code 23). Transportation Sector. The SEDS transportation sector consists of all vehicles whose primary purpose is transporting people and/or goods from one physical location to another. Included are automobiles; trucks; buses; motorcycles; trains, subways, and other rail vehicles; aircraft; and ships, barges, and other waterborne vehicles. Vehicles whose primary purpose is not transportation (e.g., construction cranes and bulldozers, farming vehicles, and warehouse tractors and forklifts) are classified in the sector of their primary use. Natural gas used in the operation of natural gas pipelines is also included in the transportation sector. The TEAM framework further refines the analysis of transportation energy consumption by classifying consumption into two activity categories: value-added and non-value-added transportation. This disaggregation allows users of TEAM to view changes in terms of total transportation energy consumption, and in terms of changes associated with commercial versus non-commercial activity. The data sources and approach for establishing this value-added/nonvalue-added allocation is taken directly from EPA s peer-reviewed CEEII analytic framework. For more information, the relevant portion of the CEEII documentation is included in A.1. Electric Power Sector. The SEDS electric power sector consists of electricity-only and combinedheat-and-power plants within the NAICS 22 category whose primary business is to sell electricity, or electricity and heat, to the public. This sector also includes electric utilities and independent power producers. Accounting for energy consumption from electric power generation and use poses a unique question not encountered for the other energy consuming sectors, namely, whether (1) to account for energy use on a primary consumption basis, that is, in the electric power sector or (2) to assign the energy use to the end-use consumption sectors of electricity. TEAM provides both ways of accounting for energy use in this sector since the different perspectives they provide could inform the findings. TEAM includes energy use on an end-use consumption basis (in addition to the primary consumption basis) because the end-use consumption basis provides a more precise understanding of electricity-related energy consumption by state than the TEAM input-output approach using total requirements coefficients. The methodology used to derive energy consumption on an end-use consumption basis, which is based on the CEEII framework, accounts for state- and electricity market region 87 -specific profiles of energy consumption for electricity production. Because these profiles differ substantially by market region, the energy use (and CO 2 emissions) resulting from a given change in economic activity and quantity of electricity consumption will also vary substantially by region. More information on the approach used to assign energy use for the electric power sector to end-use consuming sectors is included in Appendix C, which provides the relevant excerpts from the CEEII analytic framework documentation. Appendix C also includes a more detailed description of the differences between primary consumption and end-use consumption data. 87 Defined on the basis of North American Electric Reliability Corporation (NERC) regions. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 69

77 The SEDS sectors relate to the NAICS classification framework in a relatively clean, straightforward manner, as shown in Table All 4-digit NAICS sectors within a 2-digit NAICS category map to a unique SEDS sector. Table 4-21: Concordance between SEDS and NAICS sectors. NAICS Code NAICS Sectors SEDS Sector 11 Agriculture, Forestry, Fishing and Hunting Industrial 21 Mining Industrial 22 Utilities Electric 23 Construction Industrial 31 Manufacturing Industrial 32 Manufacturing Industrial 33 Manufacturing Industrial 42 Wholesale Trade Commercial 44 Retail Trade Commercial 45 Retail Trade Commercial 48 Transportation Transportation (value-added) 49 Transportation Transportation (value-added) 51 Information Commercial 52 Finance and Insurance Commercial 53 Real Estate and Rental and Leasing Commercial 54 Professional, Scientific, and Technical Services Commercial 55 Management of Companies and Enterprises Commercial 56 Administrative and Support and Waste Management and Remediation Services Commercial 61 Educational Services Commercial 62 Health Care and Social Assistance Commercial 71 Arts, Entertainment, and Recreation Commercial 72 Accommodation and Food Services Commercial 81 Other Services (except Public Administration) Commercial Carbon Emissions TEAM baseline CO 2 emissions were estimated based on baseline energy consumption values compiled from each of the three data sets described above (MECS, Economic Census, and SEDS) using carbon emission factors reported by the EIA for EIA collects and publishes time-series data on carbon emission factors by fuel type and end use. Building on the work conducted in developing the CEEII analytic framework (Abt Associates, 2005), the EIA s carbon emission coefficients were adjusted to account for: (1) the non-fuel use of carbon-containing energy inputs and (2) incomplete combustion of energy inputs. The data and methodology for performing these two adjustments are described in A.1. Because of heterogeneity resulting from the varying quality of coal used across regions and in different sectors, the EIA publishes state-specific carbon emission factors by end use of coal. These factors were used to estimate the TEAM baseline carbon emissions corresponding to state-level energy use derived from the SEDS data. The carbon emission potential of other fuels is assumed to be uniform across states. Thus, the 88 Consumption of renewable energy is accounted for, even if it is, by definition, carbon-neutral or simply not carbonbased. Renewable energy sources are assumed not to contribute to total carbon emissions, but their inclusion ensures a more comprehensive, structural representation of energy use. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 70

78 carbon emission factors for coal are both end-use and state-specific and, for all other fuels, are only end-use specific. In the case of national-level energy use data derived from MECS and U.S. Census data, the TEAM baseline carbon emissions are estimated using average emission factors from the CEEII framework (based on EIA factors) for each fuel type, including coal Integrating Energy Use and CO 2 Emissions Data into TEAM The TEAM framework is designed to generally estimate changes in emissions at the level of 4-digit NAICS sectors and states. Similar to the approach taken for other TEAM environmental impact categories, the baseline energy use and CO 2 emissions data are integrated into the TEAM framework so that economic changes from a specified trade event (at the level of 4-digit NAICS sectors) can be mapped to the energy consuming sectors and used to estimate changes in energy consumption and carbon emissions. The energy use and CO 2 emissions data, however, differ from other TEAM environmental impact categories in that they are estimated at the national-level, rather than states, or for aggregated sectors (five aggregated sectors) rather than 4-digit NAICS sectors. These differences require a distinct treatment of the energy use and carbon emissions baseline data so that results are shown at the appropriate level of detail. Thus, in model runs that include either carbon emissions or energy use as an impact type, the model presents all results (including results for other media types such as water discharges or air emissions) at the aggregated level of resolution corresponding to the source data set either the aggregated sectors defined in SEDS (when SEDSderived baseline data are specified), or the national level when MECS and Census-derived baseline data are specified Data Limitations SEDS-derived energy use and CO 2 emissions data are provided on the basis of both primary and end-use of energy. In cases where the TEAM analysis is performed on a total sector effect basis i.e., including impacts for sectors linked through the input-output matrix however, it would not be appropriate to account for energy use from the electric power sector on the basis of end-use consumption. In this analysis configuration, the TEAM I-O framework already captures the change in electricity production associated with the primary effect sectors for the TEAM analysis. Thus, using the end-use consumption framework for assigning electricity-related energy use (and CO 2 emissions) to primary consumption sectors would result in doublecounting of the impacts. Additionally, TEAM CO 2 emissions are based on energy use both as fuel or non-fuel and do not include process related emissions not associated with uses of energy sources (e.g., CO 2 emissions from cement manufacturing other than those generated through the combustion of fuel sources). 4.4 NERC-Adjusted Emissions Baseline Data An alternative compilation of the baseline emissions data set described in Sections 4.1 through 4.3 has been developed to enhance TEAM analytic capabilities by accounting for the variation in the profile of GHG emissions, energy use and other environmental impacts of electricity generation by North American Electric Reliability Corporation (NERC). 89 This regional treatment of electric power sector emissions is meant to 89 The ten NERC regions are: ASCC Alaska Systems Coordinating Council; ERCOT Electric Reliability Council of Texas; FRCC Florida Reliability Coordinating Council; HICC Hawaii Coordinating Council; MRO Midwest Reliability Organization; NPCC Northeast Power Coordinating Council; RFC Reliability First Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 71

79 support a better assessment of the GHG emissions impacts of electricity-consuming sectors, based on the specific regional profile of each sector s operations and the GHG emissions intensity of electricity produced within each NERC region. Further, it provides a similar improvement in understanding the differential burden from water and non-ghg air pollutant emissions associated with electricity consumption, based on the regional location of the electricity consuming sector. Adjustments made to the default TEAM environmental baseline include: Water and non-ghg air emissions: Entries for NAICS 2211 are reassigned to the appropriate NERC-adjusted NAICS code based on the states corresponding to each NERC region (see state-to- NERC mapping in Table A-1 of Appendix A). Consequently, for all states except Puerto Rico, instead of being reported for the total electric power sector (NAICS 2211), emissions in the new TEAM NERC-adjusted state-level water and non-ghg air pollutant emissions are now reported for each NERC-specific electric power sector (221X). Note that because there is no NERC region associated with Puerto Rico, emissions reported for the electric power sector in Puerto Rico are still reported for the total electric power sector (NAICS 2211). Energy use: TEAM provides data on energy consumption from two sources, each of which is embedded in a different baseline data file: a state-level/aggregated economic sectors data set, derived from SEDS, and a national-level/naics data set, derived from MECS (and Census) data. The following adjustments were made to align these data sets with the NERC-adjusted framework of analysis: The SEDS-derived state-level file contains data on energy consumption by five aggregate economic sectors commercial, electric power, industrial, residential, transportation and energy source coal, electricity losses, electricity, natural gas, nuclear, petroleum, and renewables. Similar to the adjustments described above, entries for sector 2211 are reassigned to the NERC-specific sector 221X using the the state-to-nerc mapping. Consequently, instead of being reported for the total electric power sector (Electric), statelevel energy use in the NERC-adjusted state-level energy use data file is reported for each NERC-specific electric power sector (Electric-NERC). The MECS-derived national-level file ( bynaics ) contains energy use data by energy source coal, coke and breeze, distillate fuel oil, gasoline, LPG and NGL, natural gas, net electricity, residual fuel oil, and other for 3- and 4-digit NAICS manufacturing and mining industry sectors. In the NERC-adjusted file, net electricity reported for 27 4-digit and digit NAICS sectors are allocated to NERC-specific sectors based on allocation factors developed based on the 2002 TEAM state-level economic baseline. This assumes that a given electricity-consuming sector purchases electricity from each NERC-specific electric power sector in proportion to the share of revenue from this consuming sector that is reported within the NERC region. As a result of this allocation, each net electricity used by each electricity consuming sector in the NERC-adjusted NAICS-level file is allocated across NERC regions (Net Electricity-NERC). GHG emissions: The SEDS-derived state-level GHG emissions file contains data on GHG emissions from primary and end-uses of energy by four aggregate economic sectors commercial, industrial, residential, and transportation sectors and from primary uses of energy by the electric Corporation; SERC Southeastern Electric Reliability Council; SPP Southwest Power Pool; WECC Western Energy Coordinating Council. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 72

80 power sector. Similar to the adjustment described for non-ghg emissions, the electric power sector is reassigned to NERC-specific sectors using the state-to-nerc mapping. Consequently, instead of being reported for the total electric power sector (Electric), state-level GHG emissions in the NERCadjusted state-level GHG emissions data file are now reported for each NERC-specific electric power sector (Electric-NERC). As a result of these enhancements, the NERC-adjusted emissions baseline files no longer include the NAICS sector 2211: Electric power generation, transmission, and distribution or Electric Power sector. 90 Instead, they contain ten distinct NERC-specific pseudo NAICS sectors 221X, Electric-NERC, or energy source categories Net Electricity-NERC. 90 With the exception of Puerto Rico, for which we retained NAICS 2211 in the absence of a NERC region. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 73

81 Chapter 5: Assessing the Environmental Impacts of Economic Changes This section describes how the model translates the economic changes associated with a trade or other economic event into changes in the environmental releases of water pollutants, air pollutants, hazardous wastes and agricultural chemicals, and changes in land use and water use by affected industry sectors. The process involves two main steps: (1) describing the trade or economic event into the TEAM framework and (2) calculating the resulting changes in emissions and resource use. These two steps are described in Sections 5.1 and 5.2, respectively. Sections 5.3 and 5.4 then describes how TEAM results are reported, and the validation of TEAM calculations. 5.1 Trade or Economic Event Description TEAM estimates the environmental impacts of a trade or other economic event. A TEAM economic event is defined as the absolute total change in the value of domestic production for each 4-digit NAICS sector. The input trade event, however, may be specified in an economic sector framework other than NAICS, for a base year that differs from the TEAM 2002 base year, and as primary economic impacts to specific sectors rather than total impacts distributed among all linked sectors of the economy. TEAM includes methods to process the economic event input to make it consistent with the TEAM framework. The steps involved may include: 1. Converting the economic event to the NAICS economic sector classification system; 2. Converting the trade event dollars base-year to 2002 dollars; and 3. Translating primary sector-specific impacts to total economic impacts, accounting for imports if desired. These pre-processing steps are described below Converting Economic Sector Classification Systems into NAICS In the current version of TEAM, the economic event is specified as a vector representing the absolute change in national-level shipments, by 4-digit NAICS economic sector. 92 TEAM has the capability to translate economic events specified in several different economic sector classification systems by using pre-defined concordance tables specifically developed for this purpose. Concordance tables currently exist for the following economic classification systems: In the prior version of TEAM, some emissions data (e.g., Permit Compliance System data) were developed using data for years other than the 1997 economic baseline year and therefore had to be adjusted to 1997 using PPI- CAGR adjustment factors. The current TEAM calculation framework uses only baseline economic data and emissions data for Therefore, no additional adjustments need to be performed at run-time to align baseline economic and emissions data in TEAM. Note that only sectors with non-zero absolute changes need to be specified. By default, sector not listed in the vector file are assumed to have no change resulting from the trade or other economic event. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 74

82 SIC: 4-digit numerical codes used to represent various sectors of economic activity. The first two digits of the code identify the major industry group, the third digit identifies the industry group, and the fourth digit identifies the industry. While SIC was replaced by NAICS starting in 1997, several data sets are still provided with SIC-based data (e.g., EPA PCS and TRI data, as described in Section 4.1). BEA 2002: 6-digit classification categories used to represent 426 industries or commodities, 93 as reported in BEA s 2002 Benchmark Input-Output Accounts. The BEA 2002 classification system is based on the NAICS sectoral framework. The economic event is described through a text input file to the model. In addition to descriptive information about the scenario being modeled, the input file header specifies the economic sector classification structure being used, the dollar year of data, and indicates whether the dollar values contained in the file represent primary or total economic impacts. The file then lists, for each sector affected by the economic event, the sector code and the absolute change in revenues or in the value of shipments, in dollars. If necessary, the economic event is translated into the TEAM native NAICS framework using the corresponding pre-defined concordance table, which maps the absolute changes specified in the input file to corresponding changes in each 4-digit NAICS sector Converting the Base-Year to 2002 Dollars In cases where the base year of analysis differs from the 2002 base-year assumed in TEAM, the model converts the specified changes in revenues to 2002 dollars to ensure consistency with TEAM emissions and resource use factors, which were developed based on 2002 revenues. The conversion of economic data to the TEAM base-year is performed on a sector-specific basis based on the change in the Producer Price Index (PPI), 94 by sector, between 2002 and the year of the economic event. The PPI adjustment coefficients used in TEAM are based on sector-specific data published by the Bureau of Labor Statistics. 95 The development of the TEAM PPI adjustment support file is described in greater detail in Appendix A. The conversion of economic event data to dollars of the TEAM base-year (2002) involves the following steps: 1. Identify the year of original impact data, e.g., Select the corresponding adjustment vector from the TEAM base-year dollar conversion file. 3. Multiply the NAICS impacts values by the conversion factors to obtain corresponding NAICS impacts in $2002. Where: Impact-NAICS i,2002 = CF i,j x Impact-NAICS i,j Impact-NAICS i,2002 = Value of the trade event impact for sector i in 2002 dollars This number excludes final use sectors and commodities and adjustments (e.g., noncomparable imports, rest of the world adjustment, taxes on production and imports, government services and enterprises). The Producer Price Index measures average changes in prices received by domestic producers for their output. The data are available at: Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 75

83 CF i,j = coefficient from base-year dollar conversion file for NAICS sector i and used to convert dollar base-year from year j to Impact-NAICS i,j = economic event value for NAICS sector i in dollars of year j. The resulting values Impact-NAICS i, 2002 are carried forward to the next step in the TEAM analysis Converting Primary Economic Impacts into Total Impacts TEAM is designed to process economic changes as either primary or total impacts. When expressed as primary impacts, the economic event provides the primary sectors in which changes in economic activity are expected to occur and the magnitude of the changes, but does not account for the indirect economic effects resulting from linkages of these primary sectors to other sectors in the economy. To understand the total impact in both economic and environmental terms of an economic event expressed as primary impacts, it is therefore necessary to convert these impacts to total impacts that account for linkages between sectors either as purchasers or suppliers of intermediate inputs to production. TEAM treats the economic event, if specified in terms of primary impacts, as a change in final demand, and uses the total requirement matrix from an input-output analysis of the U.S. economy to calculate the total change in economic activity in the economy, encompassing both the primary impact sectors and all linked sectors. The input-output analysis relates the production of a given sector to the production of all other sectors from which it uses intermediate inputs. The sector-to-sector relationships are represented by a matrix whose coefficients ( total requirements coefficients ) provide the value of the production required of each linked sector to deliver an additional dollar of output to final demand from the primary sector. The total requirements coefficients used in TEAM were developed using the Benchmark Input-Output Accounts produced by the Department of Commerce Bureau of Economic Analysis every five years. The accounts relate the inputs of BEA-defined industries to intermediate inputs from related industries at the sixdigit classification level. Tables for the 2002 benchmark were converted to the native TEAM 4-digit NAICS economic framework into a matrix of 294 x 294 elements that relate each NAICS sector to all other NAICS sectors of the economy. The resulting I-O matrix input data set consists of three variables: the input NAICS industry sector, the output NAICS industry sector, and the total requirements coefficients. TEAM includes three versions of the I-O total requirements coefficients matrix: Standard I-O matrix: The standard I-O matrix is based on the standard use and make tables published by BEA. The coefficients thus derived represent the total inputs required for each commodity produced. They assume that all inputs -- intermediate and final -- are produced domestically. As a result, this standard matrix may overestimate the total domestic economic activity and emissions and resource use effects resulting from an analysis case that is defined in terms of economic activity changes in primary impact sectors. Import-adjusted I-O matrix: This alternative derivation of total requirements coefficients accounts for the share of each intermediate inputs that are imported, and therefore do not contribute to domestic emissions. NERC-adjusted I-O matrix: This alternative derivation of total requirements coefficient accounts for the share of electricity that is consumed in each of the ten NERC regions. This allows the model to account for the different emissions profile of electricity generation across the regions. As compared to the Standard I-O matrix which defines the electric power generation sector at the national level Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 76

84 (with emissions profile and linkages to other sector based on the national aggregate of electricity generated), the NERC-adjusted matrix defines the electric power generation sector as ten individual NERC-specific sectors, each of which with its own emissions profile and direct and indirect inputs. The development of these three TEAM input-output matrices is described in Appendix A. Each file provides the estimates of inputs directly and indirectly required from each of digit NAICS sectors (specified in the first column in the TEAM support file) to deliver a dollar of each 4-digit NAICS sector s output to final users (specified in the second column in the TEAM support file). Coefficients are given in the third column of the TEAM support file. Thus, each file describes a total requirements matrix 294 x 294 in dimensions, by providing in order: the row index, column index, and total requirements coefficient (86,436 coefficients). The two tables differ in that the import-adjusted total requirements coefficients reflect only the domestic contribution of each input sector required to produce one dollar of each output sector s production to final demand. 5.2 Calculating Changes in Emissions/Resource Use TEAM uses the estimates of national level change in economic activity in 2002 dollars to calculate changes in emissions/resource use by specific pollutant/resource media, economic sector, and location (state). TEAM calculates the change in emissions/resource based on emission factors for each of three principal pollutant emissions or resource use categories described in Section Chapter 4: water discharges, air emissions, and energy use. Each emission factor is defined as the value of baseline emissions/resource use for a given pollutant/resource, state, and economic sector divided by the value of baseline economic activity measured as value of shipments in 2002 for the state and economic sector. 96 The general framework for calculating the change in emissions/resource use involves the following two steps: Disaggregating sector-level economic event impact values to the state level; and Multiplying the economic event impact value for each sector by emission factors to calculate changes in emissions/resource use. Each step is described below Disaggregating Economic Event Impact Values to the State Level The economic event data provided to TEAM are anticipated to be national in scope. Depending upon the assumptions made and/or analytic case specifications concerning the local distribution of national level economic event impacts, it may be necessary to disaggregate the national level impact values to the state level. The simplest disaggregation is one that assumes that the distribution of state-level economic event impacts matches the baseline distribution of economic activity across states within a sector. In this case, the disaggregation is performed as follows: 96 For point source air emissions and water discharges, TEAM includes emissions on the basis of true facilities, as identified in the NEI, PCS and/or TRI data sets. However, although TEAM is configured to analyze all emissions/resource use categories on the basis of true facility data, revenue data are not currently available in TEAM to support true facility analysis of emission factors. As discussed later in the section, however, facility-level emission factors are not necessary to estimate the change in emissions since the model applies the relative national change across all locations. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 77

85 For each NAICS sector, i = 1.M, and for states j = 1.N, where: Impact _ NAICS Rev i, j i, j = N Impact _ NAICS j= 1 Rev Impacts_NAICS i,j = Economic event value for NAICS sector i and state j Rev i,j = 1997 shipments/revenue in NAICS sector i and state j Impacts_NAICS i = National economic event value for NAICS sector i N j = 1 Revi, j = National 2002 shipments/revenue in NAICS sector i. Other, less simple, disaggregations are possible depending on the specification of the economic event to be analyzed. For example, if it is known that the change in economic activity embodied in an economic event will be concentrated in a specific geographical region, 97 then the disaggregation of economic event data could be constrained to only the states within that region. In that case, the disaggregation concept would be the same as outlined above, but the N states over which the economic event impacts are disaggregated would be limited to the states within the specified impact region. In general, there are certain circumstances in which disaggregation of national level economic event values to the state level is not necessary. These circumstances are when: (1) the distribution of state-level economic event impacts is assumed to match the baseline distribution of economic activity across all states in the nation (i.e., as outlined above), and (2) emission factors by 4-digit NAICS sector and state/facility are assumed to remain equal, for marginal economic and emissions/resource use calculations, to the average emission factors developed from the baseline emission factor calculation. In this case which forms the more general, default TEAM analytic case the percent change in emissions/resource use for all locations in the analysis, for any 4-digit NAICS sector, will be the same as the percent change in national economic activity by sector, as reflected in the national economic event data. As a result, the change in emissions/resource use in a given sector at the state level may be calculated simply by multiplying the baseline emissions/resource use for the sector at the state level by the national percent change in economic activity for the sector. 98 i, j i For reasons other than the simple baseline concentration of affected industries within the identified region. Possible causes of regional disparities may include the proximity to a foreign market, e.g., northern states along the Canadian border, or states along the Pacific Rim in the case of trade with Asia. As mentioned earlier in this section, TEAM includes emissions on the basis of true facilities for point source air emissions and water discharges. However, although TEAM is configured to analyze all emissions/resource use categories on the basis of true facility data, revenue data are not currently available in TEAM to support true facility analysis of emission factors. However, facility-level emission factors are not necessary to estimate the change in emissions since the model applies the relative national change across all locations. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 78

86 5.2.2 Calculating Changes in Emissions/Resource Use by Multiplying Economic Event Impact Values by Emission Factors In the TEAM general analytic framework, changes in emissions/resource use are calculated by multiplying the change in economic activity for a given 4-digit NAICS sector and state by the emission factors for the 4- digit NAICS sector and state location. This calculation is performed as follows: where: E/RU i,j E/RU i,j Rev i,j Rev i ΔE/RU i, j E/RU = Revi i, j ΔRevi = Calculated change in emissions/resource use for Team Entity i and pollutant/resource j. The Team Entity identifier, i, is the TEAM ID, which is a combination of information indicating state location and 4-digit NAICS sector. The TEAM ID for true facilities also contains information connoting and uniquely identifying the facility as a true facility Team Entity (i.e., the facility identifier from PCS, TRI or NEI). = 2002 baseline emissions/resource use for Team Entity i and pollutant/resource j. = 2002 shipments/revenue for Team Entity i = Change in shipments/revenue in 2002 dollars for Team Entity i, from the economic event dataset. In the current model framework, this is equal to the state-level change in shipments/revenue. As noted in earlier discussion, in most cases analyzed in TEAM, disaggregation and assignment of economic event data to individual states and/or facilities is not needed for the calculation of change in emissions/resource use. Instead, with (1) the national change in economic activity by sector assumed to be spread over all locations in proportion to the baseline distribution of economic activity and (2) each Team Entity s emission factors remaining fixed at the baseline, static emission factor value, the percent change in emissions/resource use for each Team Entity is equal to the percent change in national level economic activity for the entity s economic sector. In this case, the calculation of change in emissions/resource use for each Team Entity is as follows: where: E/RU i,j,(k) E/RU i,j Rev i,j Δ i, j, ( k ) = i, j, ( k ) E/RU E/RU ΔRev Revk = Calculated change in emissions/resource use for Team Entity i and pollutant/resource j and in 4-digit NAICS sector k. = 2002 baseline emissions/resource use for Team Entity i (in 4-digit NAICS sector k) and pollutant/resource j. = 2002 national shipments/revenue for 4-digit NAICS sector k Rev k = Change in national shipments/revenue for 4-digit NAICS sector k in 2002 dollars, from the economic event dataset. k Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 79

87 The values E/RU i,j are the changes in emissions/resource use that TEAM reports as the first level of environmental impact of an economic event. 5.3 Compiling and Reporting Results As a result of the calculations outlined in Section 5.2 above, TEAM generates estimates of the change in emissions/ resource use for all TEAM Entities, either true facilities (for air emissions and water discharges) or the states. Table 5-1 lists the number of individual reporting elements by pollutant/resource media category for which TEAM calculates and reports changes in emissions/resource use. Elements here refer to chemicals, compounds, or indicators of resource use. For the air and water pollution categories, in addition to calculating the change in emission/discharge for individual pollutants, TEAM calculates and reports toxicitynormalized aggregates for specific pollutant subsets in these pollutant categories. The toxicity-normalized estimates are calculated by use of toxic-weighting factors currently incorporated by EPA in the Risk Screening Environmental Indicators model. 99 Table 5-1 indicates the number of individual pollutants/chemicals for which toxic weights are currently incorporated in TEAM and for which toxicitynormalized aggregates of the change in pollutant emission/discharge are reported. Table 5-1: Reporting Elements By Pollutant/Resource Category in TEAM Reporting Elements Pollutant/Resource Category Reporting Elements (Chemicals) with Toxic Weighting Factors Point Source Air Emissions Area Source Air Emissions Mobile Source Air Emissions Indirect Water Discharges Direct Water Discharges CO 2 Emissions (SEDS-derived) 2 N/A Energy Use (SEDS-derived) 7 N/A CO 2 Emissions (MECS/Census-derived) 7 N/A Energy Use (MECS/Census-derived) 9 N/A TEAM assembles estimates of the change in emissions/resource use at the level of the individual TEAM Entity by the reporting elements as summarized in Table 5-1. Data may be organized for reporting in a wide range of frameworks, including: Aggregations by individual pollutant/resource category Aggregations of toxicity-normalized emissions/resource use Aggregations of emissions/resource use by state (or more detailed location definitions) Aggregations of emissions/resource use by industry And various combinations of the above reporting frameworks. Change in emissions/resource use may be reported both as absolute values and as change relative to baseline values. 99 Developed by Abt Associates Inc. for U.S. EPA Office of Pollution Prevention and Toxics. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 80

88 5.4 Validation Appendix E summarizes the results from a simple test case performed to verify and validate TEAM execution. The test case involves a hypothetical change in exports and imports from the principal trading sectors between the United States and Chile. In addition to testing the calculation of emissions/resource use changes, the test case also validated the various economic event processing procedures described in Section 5.1. Specifically, the test case was designed as follows: The economic event data for the test case is configured in the Bureau of Economic Analysis (BEA), thus requiring translation from the BEA framework to the 4-digit NAICS sector framework for TEAM processing. The economic event involves a reduction in economic activity in two BEA sectors related to sugar production. The hypothesized increases in economic activity are set at 50 percent of the baseline value of economic activity in the assumed affected sectors. The economic event is specified in year 1997 dollars, thus requiring conversion to TEAM base-year 2002 dollars. The economic event is specified as a primary impact event, thus requiring conversion through the Input-Output Translation framework to a total impact specification of affected sectors and impact values. The test case confirmed that TEAM performs the data manipulations and calculations as described in this documentation. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 81

89 Chapter 6: Interpreting Environmental Impacts TEAM estimates the increases in the emissions or resource use associated with each 4-digit NAICS sector at the state level. These impacts are expressed either in absolute (e.g., total pounds emitted), or relative terms (percent increase/decrease from baseline values). This allows for a detailed evaluation or drill-down of the impacts associated with specific chemicals. Understanding the overall significance of emissions change from a human health and welfare perspective, however, involves further analyses that consider the fate and transport of these pollutants in the environment, which determines the exposure potential, and their relative toxicity and associated health risks. The significance of emissions changes may also be informed by a parallel evaluation of existing environmental conditions, including considerations of assimilative capacity or of pre-existing impairments of natural resources. Although TEAM does not explicitly calculate human health and welfare impacts, the model provides a framework that builds on, and complements, existing health and welfare models. For instance, TEAM has the ability to weight the predicted changes in emissions of various chemicals in terms of their relative toxicity. It also provides an interactive view of the expected changes in emissions, in parallel to pre-existing ambient conditions expressed in terms of attainment of air quality and water quality criteria. The emissions predicted by TEAM could serve as inputs to fate and transport models to estimate long-term ambient concentrations, and determine human health impacts or evaluate changes in the attainment of air or water quality criteria. 6.1 Human Health and Welfare TEAM accounts for and predicts emissions of a large number of chemicals. As a result, to provide meaningful interpretation of the estimated changes in releases, by chemical, reported in a TEAM output, a means of differentiating chemicals based on their relative hazard is needed. TEAM currently uses toxicity weights to reflect the relative human health impact of pollutants. The TEAM toxicity weights are based on the methodology used in EPA s Risk Screening Environment Indicators (RSEI) model. The RSEI toxicity weights consider cancer and non-cancer risks in weighting different chemicals. The concept is described below Risk-Related Results The Risk Screening Environment Indicators model (RSEI) uses risk concepts to provide an analytical screening-level overview of the relative risk of pollutants and their potential chronic health impacts. The risk-related impacts are a function of chemical toxicity, environmental fate and transport, exposure pathways, and the number of people exposed. RSEI provides numerical values that indicate the relative populationlevel risk of exposure for a given chemical, and exposure pathway. The RSEI risk metric neither has an absolute impact interpretation (e.g., number, or change in probability, of some specified adverse health events in the exposed population) nor can be interpreted relative to an absolute risk impact threshold (e.g., values exceeding a recommended maximum exposure level). The values provided are only meaningful when compared with other values produced using this same methodology. This said, this information provides an additional level of understanding to the TEAM results by allowing the comparison of relative hazards of different chemicals. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 82

90 The model reflects the toxicities of chemicals relative to one another using a continuous system of numerical weights whose value increases as the toxicological potential to cause chronic human health effects increases (approximate range 0.01 to 1,000,000). The method separately evaluates exposure routes (inhalation and oral) and classes of effects (cancer and non cancer), but uses the highest number as the final weight (i.e., worst case). For TEAM, we expanded the list of RSEI toxicity weights, which initially included only TRIreportable chemicals, to cover all chemicals reported in the TEAM emissions baseline that have the required information. Approximately sixty percent of the chemicals listed in TEAM (617 of 1,075 chemicals) have toxicity weights. The development of toxicity weights for TEAM followed the same approach used for RSEI, i.e., based on toxicity data available in various sources, with priority given to data from EPA s Integrated Risk Information System (IRIS) and Office of Pesticide Programs Reference Dose Tracking Reports Getting from Hazard to Risk In TEAM, the toxicity weights are used to scale the impact of the predicted change in emissions of the various pollutants, thus providing a measure of relative hazard. Hazard-related results are obtained by multiplying the toxicity weight by the change in emissions. Thus, while certain pollutants may be expected to increase by only small quantities, in absolute terms, their greater toxicity may pose a greater potential hazard from a human health perspective. This information, however, does not provide an estimate of risk. Getting at a measure of risk, even for a screening level assessment involves not only looking at the toxicity of the pollutants, but also exposure. The second component of risk assessment thus involves determining to what degree the pollutant is available in the environment, i.e., at what concentration, what population may be exposed and for how long and at what dose, and how susceptible is the population to the pollutant effects. As mentioned above, TEAM does not provide this assessment and risk must instead be evaluated using other complementary models that focus on specific issues and media, such as REMSAD (air quality), BenMAP (health benefits), RSEI (air and water dispersion), etc. 6.2 Environmental Baseline and Ambient Conditions TEAM can currently present the estimated changes in pollutant releases in conjunction with selected indicators of ambient environmental quality to enhance the understanding of changes in emissions and resources use predicted by the model. This section describes the development of the datasets for each of the two media covered in the environmental baseline: air and surface water. This environmental baseline, as opposed to the emissions/resource use baseline described in Section Chapter 4, focuses on measured pollutant concentrations and on qualitative assessments of ambient conditions based on ecosystem response or applicable water or air quality standards, e.g. water use impairment and air quality criteria attainment status. In contrast to other TEAM baselines, which have a base year of 2002, the environmental baseline was developed using the most recent assessment of ambient conditions available at the time. This was done purposefully to provide the most relevant point of reference for TEAM analyses, which are most likely to focus on future conditions. Moreover, the environmental baseline is not used in performing model calculations, but rather to provide a context to the TEAM results, and therefore does not need to align in time with the input data files. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 83

91 6.2.1 Baseline Water Quality Data Baseline water quality data are available at the national level through the various databases maintained by EPA. Data include both quantitative monitoring data (e.g., STORET), and qualitative assessments. Given the large amount of monitoring data available and the difficulty of meaningfully integrating such data in the TEAM framework, we focused our efforts on available qualitative data. This qualitative data implicitly integrates detailed quantitative characterization of ambient conditions and ecological response. We found the best source of national-level water quality assessment data to be the reports published under Sections 303(d) and 305(b) of the Clean Water Act. These reports cover all states and follow a standard national format Section 303(d) Report The purpose of the so-called 303(d) report is to fulfill the requirements set forth in Section 303(d) of the Federal Clean Water Act (CWA) and the Water Quality Planning and Management regulation at 40 CFR Part 130. The report is submitted by each state to EPA for review and approval of the state s list of quality limited or threatened waters needing established pollutant Total Maximum Daily Loads (TMDLs). Section 303(d) of the CWA requires states to: Identify waters that will not attain applicable Water Quality Standards (WQS) with technology-based controls. This includes treatment technology, best available technology and Best Management Practices enforced by federal, state, or local laws or regulations. Identify waters that are threatened, which means those waters that presently attain WQS but are expected to exceed standards. Establish a priority ranking (schedule) for TMDLs so that they are completed in the next 8 to 13 years. The national 303(d) dataset is available on EPA s Watershed Assessment, Tracking & Environmental ResultS (WATERS) website. The database provides, for each waterbody assessed, the waterbody identifier, name and type, the type of impairment as identified by the state, and its equivalent EPA category. Table 6-1 lists the 10 most often stated reasons for water impairment. Impairments that we believe most likely to be affected by pollutant releases estimated in TEAM are shown in bold type. Table 6-1: Most Often Stated Causes of Impairment in 303(d) List Rank EPA Impairment Designation (state descriptions) Impairment Count (Total: 43,044) 1 Sediment/Siltation (suspended solids, turbidity) Pathogens (high fecal coliform count, e. coli, beach closures) Metals (aluminum, arsenic, chromium, copper, iron, lead, manganese, nickel, 5045 selenium, silver, zinc, etc.) 4 Nutrients (algal growth/blooms, chlorophyll-a, macrophytes, nitrogen, 4794 nitrate/nitrite) 5 Organic Enrichment/Low DO (eutrophication, high BOD, hypoxia) Other Habitat Alterations (loss of instream habitat, stream bank 2224 destabilization, inadequate fish passage, wetland loss) 7 Thermal Modifications PH Pesticides (atrazine, chlordane, diazinon, dieldrin, DDT, endosulfan, 1590 toxaphene, tributyltin) 10 Fish Consumption Advisories (fishing or shellfishing ban) 1260 Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 84

92 Section 305(b) Report The purpose of the Section 305(b) report is to present to the U.S. Congress and the public the current conditions of the nation s waters. CWA Section 305(b) requires each state to prepare a water quality assessment report every two years. EPA compiles the information from the state reports and prepares a summary on the status of the nation s waters. The national 305(b) list is available at EPA s WATERS website. The latest final dataset available at the time this functionality was incorporated in TEAM reflect the 2000 assessment cycle and provides, for each waterbody assessed by states, the attainment status for designated water uses, cause(s) of impairment, and suspected source(s) of impairment. 100 Waterbodies are identified by their ID, name and state. The national 305(b) list contains a total of 117,010 assessments, in which 75,612 waterbodies are classified good, 36,521 impaired, and 4,875 threatened. Table 6-2 lists the 10 causes of impairment most often stated. Impairments that are most likely to be affected by pollutant releases estimated in TEAM are shown in bold type. Table 6-2: Most Often Stated Causes of Impairment in Section 305(b) List Impairment Count Rank EPA Impairment Designation (state descriptions) (Total: 36,521) 1 Pathogens 15,167 2 Sediment/Siltation 7,477 3 Organic Enrichment/Low DO 6,357 4 Nutrients 6,207 5 Metals 5,226 6 Other Habitat Alterations 3,711 7 Sulfates 2,848 8 Salinity/TDS/Chlorides 2,817 9 ph 2, Flow Alteration 1,747 The 303(d) and 305(b) data help answer key questions: Use. Does the waterbody support its designated uses? The waterbody is assessed, for each designated use (agriculture irrigation, primary contact recreation (swimming), fishing, public water supply, etc.), as to whether it fully, partially, or does not support the use. Assessment. Is the waterbody impaired? If the waterbody doesn t fully support all its designated uses, then it is classified as impaired. If it fully supports all uses, it is classified as good. Cause. If impaired, what are the causes of this impairment? The specific physical or chemical characteristics responsible for the impairment are identified (high temperature, low DO, high mercury concentration, etc.) Source. If impaired, what are the likely sources of the pollution? Contributors to the pollution are identified (urban runoff, industrial point sources, intensive animal feeding operations, natural sources, etc.) 100 The environmental baseline data have not yet been updated to the most recent final data sets available from EPA. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 85

93 Presenting the baseline water quality data in TEAM required establishing a consistent geographical framework that could be related to TEAM results. TEAM uses the state as primary geographical units for reporting changes in pollutant releases and resource use. Waterbodies, and rivers and streams in particular, however, do not fit well within state political boundaries and are better aggregated to physical features such as watershed or hydrologic units. In developing the TEAM environmental baseline dataset for water, we used the USGS hydrologic units as defined by 8-digit Hydrologic Unit Codes (HUC) and using a combination of data sources, identified the HUC corresponding to each waterbody in our water dataset. We then aggregated the baseline water quality data to the HUC level by calculating the percent of waterbodies classified as impaired, by cause. This information comes mainly from the 305(b) list, which also reports good and threatened waterbodies. These hydrologic units were later mapped to the TEAM results by identifying states intersected by each 8- digit HUC based on a geographical concordance we developed in ArcView between GIS coverages of states and the coverage of 8-digit hydrologic units obtained from the USGS. The percent of assessed waterbodies that are impaired helps contextualize the TEAM results by providing, for example, for the hydrologic units located within a state, the frequency of impairments due to pollution by metals. Note, however, that since not all waterbodies are assessed within a given two-year cycle, this percentage only represents the fraction of waterbodies impaired within the subset of waterbodies assessed. If we assume, however, that the waterbodies assessed within any given cycle are representative of the overall population, this value can be considered a reasonably accurate indicator of water quality within the county or state Baseline Air Quality Data The objective of integrating baseline air quality data is to provide a perspective on how predicted emissions relate to ambient air quality. Available baseline air quality data comprise qualitative attainment assessments, and quantitative monitoring data. One difficulty of relating available air quality data to TEAM emissions lies in the difference in the parameters being reported. Air quality monitoring programs predominantly report pollutants that are known to have acute and chronic health effects, but are often not the direct emissions. Ozone and particulates, for example, primarily result from the transformation and interaction of precursor pollutants in the atmosphere. Ground-level ozone is formed by the chemical transformation of certain airborne pollutants, particularly VOCs and NOx, in the presence of sunlight. And while some fine particles are emitted directly, the most important are formed in the atmosphere through the chemical conversion of gaseous pollutants. They include sulfates (from sulfur dioxide), nitrates (from nitrogen oxides), and organic aerosols (either emitted directly or from VOCs). The baseline air quality data used in TEAM were developed from two national databases of county-level data. Each source is described below Nonattainment Areas for Criteria Pollutants EPA s Green Book Nonattainment Areas for Criteria Pollutants lists the counties where pollution levels persistently exceed national ambient air quality standards. Nonattainment is defined relative to the relevant standards for each pollutant, which establish ambient concentration limits for specific time periods for measurements. For instance, coarse particulate matter (PM 10 ) has an annual standard of 50 μg/m 3 (arithmetic mean) not to be exceeded in a calendar year, and a 24- Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 86

94 hour standard of 150 μg/m 3 (non-overlapping hourly average), not to be exceeded more than once in a calendar year. The Green Book provides the annual nonattainment status for urban areas throughout the country for the period. Although the list is presented by county, the data are actually aggregated by urban areas i.e., a county may have one or more urban area(s) reported. To transfer this information into the framework discussed above and used in TEAM (i.e., state level), we aggregated the areas within each county using two different metrics: Worst Case. The county is represented by its worst urban agglomeration. If at least one area within a county has a nonattainment status in a given year, the county s status is also nonattainment (binary information). This ignores the fact that a given county may have more than one urban agglomeration that is in violation of air quality standards. Total. The county is assigned the total number of areas with nonattainment status. If, for example, two of three areas of a county have nonattainment status in a given year, the county is assigned a score of 2. The drawback of this approach is that it may tend to show more favorably counties with only one large urban area when compared to larger counties. TEAM displays this second metric when comparing model results to the ambient air quality baseline. The data are included in TEAM for individual counties within each state since this level of resolution was readily achievable from the primary data source. In presenting the data, the TEAM interface lists all counties within a state, along with their respective statistics. The GreenBook reports the status of 510 counties that were classified in nonattainment at least once between 1992 and As shown in Table 6-3, Ground-level ozone, carbon monoxide and coarse particulate matter (PM 10 ) were the air pollutants most often cited for nonattainment between 1992 and In 2002, ground-level ozone and particulates were the most often cited cause of nonattainment. Table 6-3: Nonattainment Status Statistics Number of counties with nonattainment status Pollutant CO Lead 12 3 NO2 4 0 Ozone PM SO The nonattainment metrics discussed here are by definition binary an area either meets or doesn t meet air quality standards. Thus, although nonattainment data provide useful information on areas where air quality fails to meet standards established for common air pollutants, they do not provide information on areas that are currently meeting air quality standards but are close to exceeding those standards Criteria Air Pollutant Data Quantitative air quality monitoring data are available for urban agglomerations, counties and states and are summarized by EPA in the annual Air Trend report. Similar to data on nonattainment areas, the air monitoring data were developed at the county level and for When presenting the baseline environmental data, the TEAM interface displays data for all counties within a selected state. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 87

95 EPA s Air Trends report presents peak air quality statistics by county for the six criteria air pollutants (see Table 6-4 for definition of peak values, applicable NAAQS, and data inventory). Table 6-4: Peak Air Quality Statistics and Applicable NAAQS Parameter Peak Statistics Applicable NAAQS Number of Counties 101 CO Highest second maximum non-overlapping 8-hour concentration 9 ppm 242 Pb Highest quarterly maximum concentration 1.5 μg/m NO 2 Highest arithmetic mean concentration ppm 209 O 3 Highest second daily maximum 1-hour concentration 0.12 ppm 690 Highest fourth daily maximum 8-hour concentration 0.08 ppm 690 PM 10 Highest weighted annual mean concentration 50 μg/m Highest second maximum 24-hour concentration 150 μg/m PM 2.5 Highest weighted annual mean concentration 15 μg/m Highest 98th percentile 24-hour concentration 65 μg/m SO 2 Highest annual mean concentration 0.03 ppm 318 Highest second maximum 24-hour concentration 0.14 ppm 316 As Table 6-4 shows, data are available for only a subset of air quality parameters, with ground-level ozone and particulates (PM10 and PM2.5) being the parameters most frequently monitored Informing TEAM Analysis using the Environmental Quality Baseline TEAM estimates of pollutant levels resulting from economic changes raise a number of questions. For example: If TEAM predicts an x percent increase of metal emissions in a given state, and waterbodies within the state are either already impaired or threatened due to metals pollution, then how should we evaluate the increase? What if the waterbodies are currently in good condition and support all their designated uses? The same questions can be raised regarding air quality impacts: Is a predicted y percent increase in emissions of a given pollutant of concern if it occurs in a county that has otherwise good air quality? What if air quality is already poor? The purpose of integrating the environmental baseline within the TEAM framework is not to provide definitive binary answers to these questions, but rather to identify potential areas for concern and highlight the needs for further, more detailed, analyses. This is achieved by providing information that is relevant and can be readily interpreted Expected Effects of Emissions on Ambient Conditions Going further in the analysis i.e., to evaluate the impacts of incremental emissions on impairments and nonattainment assumes that resulting changes in ambient concentrations can be reliably estimated, since criteria for impairments or nonattainment are typically defined in terms of ambient concentration. If we take the case of air quality, this would require estimating the change in concentration resulting from changes in emissions, and using a baseline concentration, calculating a final ambient concentration, assuming that all other contributors remaining constant (e.g., other anthropogenic and natural non-point source emissions). This final concentration could then potentially be compared to air quality standards and/or used to estimate human health impacts. As discussed in Section 6.1, TEAM does not currently support fate and transport 101 Out of a total of 1,096 counties reported. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 88

96 within the model, but the TEAM results can be used as inputs to other models that would allow the calculation of ambient concentrations resulting from the economic changes. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 89

97 Chapter 7: Assumptions and Key Uncertainties A number of assumptions are made in the TEAM conceptual framework (Chapter 2), the development of underlying economic and environmental baselines data (Sections Chapter 3 and Chapter 4), and the use of these data to estimate environmental impacts (Section Chapter 5). These assumptions determine the economic changes, and consequently the environmental impacts, that the model captures, and have implications for evaluating the information provided by the model. This section discusses key assumptions and their implications. 7.1 Imports vs. Domestic Production The input-output framework used in TEAM represents a semi-closed economy capturing the domestic production of goods and services. By default, TEAM assumes that increases (or decreases) in the production of intermediate inputs that are required to support an increase (or decrease) in the demand for a given domestic goods are fulfilled by domestic manufacturers, as reflected in the standard I-O total requirements coefficients. The model provides an alternative derivation of total requirements coefficients, however, that accounts for the share of intermediate inputs that are imported. Using this alternative I-O derivation allows the user to consider the increase (or decrease) associated only with the share of the production that is produced domestically. The import-adjusted I-O derivation assumes that the ratio of imports to domestic production remains constant at historical levels for each intermediate input. The model framework does not implicitly capture the displacement of domestic industry production by imports. While the model provides import-adjusted total requirements, these requirements are constant, i.e., the share of imports for each commodity does not change such that relatively more or less of a commodity is produced domestically. These impacts must be accounted for explicitly off line when describing the trade event. TEAM focuses on the increases and decreases in domestic production that (1) occur in those economic sectors that are directly affected by a trade agreement or other national level economic event and (2) occur in economic sectors that are linked to those directly affected sectors. TEAM does not account for changes in domestic production activity that occur because of an increase or decrease in imports that may also be part of the initial economic event being modeled in TEAM. As a result, special attention should be paid to adequately describe trade cases that are expected to lead to increases in imports, for example by accounting for increases in the domestic sectors that provide support services to the importing sectors. For instance, an increase in steel imports resulting from trade liberalization would not only reduce the domestic production of steel, but could also cause an increase in the domestic transportation activities that are required to bring the imported steel to intermediate and final users. Thus, although the reduction in domestic steel production implies a reduction in emissions directly associated with the sector, this reduction may be offset in part by greater emissions from commercial marine vessels and trucks used to transport the imported steel. Moreover, the environmental impacts of transportation activities may have a very different spatial distribution than the impacts related to steel manufacturing. TEAM can capture the net emissions (decrease from steel manufacturing, offset by transportation) and the regional distribution of these emissions only if the total economic impacts to both the manufacturing and the transportation sectors (and other import-related sectors) are specified in the input trade event. TEAM does not capture increases in environmental emissions resulting from activities occurring outside the political boundaries of the United States, for example as resulting from increased production of imported Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 90

98 intermediate or final inputs to meet U.S. demand. This is not a significant modeling limitation since the objective of the model is to estimate specifically the environmental impacts of trade events on the U.S. environment. However, we recognize that certain environmental impacts considered by the model most notably carbon emissions have global implications. 7.2 Scalability of Environmental Impacts As discussed in Sections 2.2 and Chapter 5, TEAM assumes a fixed coefficient relationship with an elasticity of 1 between production levels and emissions, i.e., a constant emission factor. This constant emission factor implies that increases in pollutant emissions or resource use are linearly proportional to increases in production, such that a doubling of production would also result in a doubling of emissions. The emissions factor is constant not only in time i.e., the model does not capture potential improvements in technology or methods of production but also in the scale of production i.e., the marginal emissions associated with the production of each additional unit are equal to the average baseline emissions. Figure 7-1 presents various possible forms of relationship between emissions and economic activity. The line shown in bold represents the emissions factor assumed in TEAM. As noted in Section 2.2, which presented a simplified version of this figure, this average emission factor lumps together the fixed and variable components of emissions change in relation to changes in output. The fixed component of emissions may be relatively more important in sectors that have a higher proportion of fixed costs/activities not directly related to the level of production 102. Figure 7-1: Conceptual Relationships between Economic Activity and Emissions Emissions (lb/year) EF Fixed component of emissions Value of Shipments ($/year) 102 Fixed emissions are emissions that do not vary directly with the level of production, but occur as a result of being open for business, e.g. emissions associated with space heating. Variable emissions are emissions that vary with the level of production, e.g., quantity of fuel or electricity consumed, emissions from raw chemical inputs. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 91

99 Improvements in technology and methods of production are not expected to significantly affect emission factors over relatively short time periods with duration depending on the sector, but perhaps spanning a few years and at the level of states and 4-digit NAICS sectors. The limitations of using a linear relationship between production levels and emissions may be consequential, however, in the case where a relatively large increase in demand is expected in a given sector, since the corresponding large increase in production may spur lumpy capital investments and result in a shift in the technology and vintage of the equipment used in the production process, and consequently cause changes in emission factors. Although TEAM currently assumes a linear, fixed emission factor, the framework could be adjusted in the future to account for marginal changes in emissions that differ from the observed average relationship between emissions and production value. Such adjustments could be developed from an empirical analysis of the change in emissions over time in relation to real value of production at the facility level. 103 One possible adjustment framework, which has been used in other, similar models of the relationship between production value and production inputs, assumes that emissions vary on the margin in relation to changes in real value of production following a constant elasticity function but with the elasticity able to be different from one (as is now assumed in TEAM). Such a functional form and adjustment framework could be accommodated in TEAM as part of future developments. 7.3 Spatial Distribution of Economic Changes and Environmental Impacts As discussed in Sections and 5.2.2, the general framework used in TEAM, which relies on state-level emission factors, allows for the calculation of changes in emissions/resource use resulting from changes in the economic activity occurring at this same level of detail. The framework can thus potentially capture regional differences in the spatial distributions of economic changes and of their environmental impacts. As the framework currently implemented in TEAM, however, the change in emissions/resource use in a given sector at the regional level is calculated by multiplying the baseline local emissions/resource use for the sector by the national percent change in economic activity for the sector. This assumes that the local percentage change in economic activity is the same as the national percentage change, and thus that economic impacts of a trade or other economic event are spatially distributed in proportion to the relative level of baseline activity across states. Likewise, environmental impacts are spatially distributed in proportion to the relative level of baseline emissions associated with the sector among states. For example, if the TEAM economic event specifies a 10 percent reduction in the national total value of shipments from the synthetic rubber manufacturing sector (NAICS ), this 10 percent reduction is applied uniformly to all states that report activity in that sector such that the resulting value of shipment in any given state for that sector is 90 percent of its baseline value. This simplification of the framework was implemented based on the expectation that most economic events modeled in TEAM would be national in scope. It does not reflect a limitation of the general framework used by the model. The framework, in its general form, can capture regional variations if the input economic changes differentiate the impacts by region. 103 The database of TRI chemical releases by facility could provide data for such an analysis. This analysis would seek to identify: (1) Chemicals that are used in, and emitted by, an industry in an essentially fixed proportional relationship to production, as currently assumed by the model, and (2) Chemicals whose level of use and emissions vary in some different way in relation to production value. It should also be possible to understand the character of this difference e.g., constant, but different from (and probably less than) one, elasticity. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 92

100 7.4 Sector Mapping The development of the economic and emissions/resource use baseline data sets required the mapping of data to the 4-digit North American Industry Classification System (NAICS) framework on which TEAM is currently based. This mapping process may have resulted in the reallocation of data among industry sectors. This potential source of uncertainty is of greater concern for the emissions/resource use baseline, since some of the original sources used in developing this dataset use classification systems other than NAICS to characterize the economic activities associated with reported emissions. In most cases, and particularly where SIC codes were provided, the mapping of the native industry classification to the NAICS sector framework identified aggregate NAICS sectors that are reasonably equivalent in concept and in scope, and the reallocation between economic sectors are likely within the same 4-digit NAICS family. In other cases, the sector classification used in the primary data source is based on significantly different concepts of physical processes (e.g., combustion) that do not map cleanly to specific economic sectors, but may map to entire industries. Distributing these emissions among the potential economic sectors in proportion to their relative revenue, as was done in developing the TEAM baseline data set, likely resulted in a different distribution of emissions than if one had used the physical unit of production. 7.5 Environmental Impacts not Reflected in TEAM Results TEAM uses revenue or value of shipments to characterize the level of economic activity responsible for observed emissions or resource use. As discussed in Chapter 3, several economic sectors are currently not included in TEAM because they were outside the scope of sources used in compiling the baseline economic data (i.e., 2002 Economic Census and Census of Agriculture). Certain of these sectors for example, logging or rail transportation have non-negligible levels of pollutant emissions or discharge (and energy use) and TEAM results therefore may therefore significantly under-estimate the environmental impacts of economic changes that directly or indirectly affect these sectors. Additionally, TEAM currently does not characterize non-value added sectors such as households. While emissions reported for these sectors are included in the TEAM baseline emissions data set, the lack of corresponding baseline economic activity means that the model does not currently estimate associated increases in emissions. Finally, whereas TEAM includes carbon dioxide emissions associated with energy inputs (fuel and non-fuel) to the manufacturing and other sectors of the economy, it currently does not account for non-energy process related emissions of GHG such as carbon dioxide emissions from cement manufacturing (other than those resulting from fuel inputs) or methane emissions from animal production. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 93

101 Chapter 8: Modeling Interface The current version of TEAM has two components: TEAM.EXE: the execution module, which performs the environmental impact assessment. TEAM Reporter: an interactive tool for managing and viewing TEAM output. TEAM Reporter generates various tabular summaries and display results in a geographical mapping framework. 8.1 System Requirements and Input/Output Files For efficient use of both components of TEAM, we recommend the following computer configuration: Windows 2000 or higher 2.6+ Ghz Intel-compatible, Pentium IV or higher CPU (two processors recommended) 1 GB of RAM (2 GB would improve the run-time) Standard class video card RAID controller Three 60-80GB drives (4 or more may be needed during modeling) TEAM will execute on a less powerful system. However, run time will necessarily increase with a slower CPU, less memory, or less data storage capacity. Insufficient data storage capacity may cause execution to fail Run Time Considerations TEAM s run time depends on the aggregation levels specified for a given run. Several options of execution and results aggregation are available: By sector: aggregated over all sectors and NAICS sector level. By region: national and state. By chemical: aggregated over all chemicals and chemical-specific. The finest level of resolution i.e., results calculated and reported by individual NAICS sector, state, and chemical obviously takes the most time. In our experience, however, even a full, maximum resolution run should take only a few minutes to complete. Aggregating to the national level or over chemicals further reduces run time. In addition to the various levels of data aggregation, TEAM provides an option for analyzing only a selected subset of data i.e., selected emission types/resource categories and economic sectors. Limiting execution to a subset of the full potential coverage can materially reduce run time. 8.2 TEAM Execution Module The TEAM execution module ( main program), is the primary interface for setting up and running TEAM analyses. The interface allows the user to specify the location of key input files, and set run configuration variables such as the geographical level of analysis (state or counties), and the type of impacts to be considered. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 94

102 8.2.1 Input/Output Files Economic Event File The structure of the economic event file, containing changes in national-level shipments by economic sector, is as follows: Line 1: economic sector structure of the economic event data: NAICS, SIC or BEA. Other conversion files can be easily added to TEAM. For 4-digit NAICS events, this first line should contain NAICS4. Line 2: year of the economic event data (4-digit number). Line 3: dollar units of economic event data: 1 if dollars, 1000 if thousands of dollars, or if million of dollars. Do not leave this line blank if the units are dollars. Line 4: indication if the data are primary or total impact data: PRIMARY or TOTAL. Primary impact data are mapped through the TEAM input-output framework to calculate the total requirements effects of the economic event. Line 5 and below: economic sector and change in value of shipments for that sector (negative value for decreases and positive for increases). The economic sector starts in column 1 and the economic change value starts in column 21. Save as a text file. In the example below, the input economic change data are: in the NAICS framework, for the year of 2002, in thousands of dollars, and primary impact data. The economic event values indicate the decrease in the value of shipments in the NAICS cane sugar refining and beet sugar manufacturing sectors by $1,596.8 and $1,306.4 million, respectively. Example: Line 1: NAICS4 Line 2: 2002 Line 3: 1000 Line 4: PRIMARY Line 5: Line 6: Additional TEAM input files may be specified as needed, depending on the structure of the case. These files are distributed with the TEAM software. They include: Concordance tables used to map the input trade event provided to the NAICS framework; PPI adjustment files used to align the scenario year with the 2002 base-year used in TEAM; State-level economic baseline; and Standard, import-adjusted, and NERC-adjusted input-output total requirements matrices used to translate primary impacts into total impacts. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 95

103 Emissions Baseline File In addition to the economic event and classification system concordance files, several other input files need to be pointed to when specifying the TEAM scenario, including the baseline emissions file. Two baseline emissions files are distributed as part of the TEAM installation, the first file, named TEAMPdox_BySEDS.db, contains the baseline data to be used when SEDS-derived energy use or carbon emissions baseline data are desired, while the second file, named TEAMPdox_ByNAICS.db, contains the baseline data to be used when MECS/Census-derived energy use or carbon emissions baseline data are desired. Both files contain the same water discharge and air emissions data and either can therefore be used when running a case involving these media types TEAM Impact Types The user must specify the impact types to be considered in the TEAM scenario. These impact types include air emissions (point, area, and mobile sources), water discharges (direct and indirect dischargers), energy use (based either on SEDS or MECS/Census data), and carbon emissions (based either on SEDS or MECS/Census data). Additionally, as described in Section 6.1.1, TEAM has the option to estimate air emission and water discharge impacts on a risk-weighted basis. Note that selecting either energy use or carbon emissions as impact types in a TEAM run will result in all results being displayed at an aggregated level of detail consistent with these media types, including results for other impact types generally available for states and 4-digit NAICS such as air emissions or water discharges. For example, a run that includes SEDS-derived energy use baseline data will have all results aggregated by state and by the SEDS aggregated sectors. Conversely, if MECS/Census-derived carbon emissions or energy use impact types are specified, results will be shown in TEAM Reporter (see Section 8.3) at the national level. Therefore, to generate results at the level of states/4-digit NAICS for the media types where these levels of details are available (air emissions and water discharges), the user should exclude both carbon emissions and energy use from the scenario specifications; the carbon emission and energy use impacts may instead be run in a separate analysis TEAM Execution Output TEAM Reporter Input File As a result of execution, TEAM generates five output files (under the same name with different extensions) and saves these files in the TEAM User directory. The.dat file (one of the five files) serves as an input file in TEAM Reporter. If TEAM Reporter is used on a different machine from where TEAM was run, all five output files must be saved in the TEAM User directory for TEAM Reporter to function properly. 8.3 TEAM Reporter The TEAM Reporter interface allows the visualization and manipulation of TEAM results. The interface allows the user to view, sort, and query TEAM results in a variety of ways and to display the results either in tabular form, or graphically as maps. Only key features are described below. For a more detailed description of the interface, including instructions on how to access each feature, the reader should refer to the User Manual distributed with the model. The user may view one set of TEAM results at a time, or view the differences between two sets of results, for example to evaluate the differences in emissions between a baseline scenario, and a second user-defined alternative scenario. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 96

104 The user can view the economic event file as it is defined and used within TEAM, i.e. the total absolute and relative changes in economic output for each 4-digits NAICS sector (or for aggregated sectors in cases where either SEDS-derived energy use and carbon emissions impact types are specified). The results file contains a large number of records, corresponding to each state or national, 4-digit NAICS sector (or aggregated sector, as appropriate), environmental impact type, and pollutant or indicator. The TEAM Reporter interface allows the user to sort the data sequentially in terms of the severity of impacts observed by region, sector, and chemicals. This drill-down capability enables the user to quickly extract from the large data set of results, for example, the 10 sectors associated with the most severe increases in air emissions, and the 10 regions and 10 chemicals corresponding to each of these sectors. Complete results or selected subsets can be exported to comma-delimited files for additional formatting and manipulations in other software programs. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 97

105 Chapter 9: References 1. Abt Associates, Inc. (2000). Environmental Input-Output (EIO) Model. Draft Documentation, October 23, Abt Associates, Inc. (2004). Trade and Environmental Assessment Model: Model Description, Report prepared for the U.S. EPA National Center for Environmental Economics under Contract EPA 68-W , April 14, Abt Associates, Inc. (2005). Carbon Emissions Economic Intensity Index: Development and Technical Enhancements. Report prepared for the U.S. EPA State and Local Capacity Building Branch under Contract EPA 68-W , December Ayres, R. U., and A. V. Kneese (1969). "Production, Consumption and Externalities." American Economic Review 59, no. 3: Baumol, William J., and Wallace E. Oates (1988). The Theory of Environmental Policy, Second edition, New York: Cambridge University Press. 6. BEA (2006). Concepts and Methods of the U.S. Input-Output Accounts. Report prepared by Karen J. Horowitz and Mark A. Planting. September Duchin, F. and A. Steenge (1999). Input-Output Analysis, Technology and the Environment, in J. van den Berg (ed.), Handbook of Environmental and Resource Economics, Edward Elgar, Cheltenham, UK, pp E.H. Pechan and Associates (2001). Economic Growth Analysis System, Version 4.0 Reference Manual, Final Draft. Prepared for Gregory Stell (MD-14), Emission Factor and Inventory Group, Emissions, Monitoring, and Analysis Division, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC January 26, Abt Associates received an electronic version of Table B-1 as physout_module_crosswalk_sic_updated.xls dated 1/22/01 from E.H. Pechan. 9. Kneese, Ayres, and d Arge (1970). Economics and the Environment: A Materials Balance Approach, Resources for the Future, Inc., Washington DC. 10. Leontief, W. W. (1986). Input-Output Economics. 2nd ed., New York: Oxford University Press. 11. Leontief, W. W. (1953) et al. Studies in the Structure of the American Economy, Oxford University Press, New York 12. Leontief, W. W. (1951). The Structure of American Economy, , Oxford University Press, New York. 13. Miller, R. and P. Blair (1985). Input-Output Analysis: Foundations and Extensions, Prentice-Hall, NJ. 14. National Research Council. (2006). Analyzing the U.S. Content of Imports and the Foreign Content of Exports. Committee on Analyzing the U.S. Content of Imports and the Foreign Content of Exports. Center for Economic, Governance, and International Studies, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press Stanley-Allen, K., N. R. Empey, D.S. Meade, S. J. Rzeznik, M. L. Streitwieser, and M.S. Strople (2005). Preview of the Benchmark Input-Output Accounts for Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 98

106 Appendix A Development of TEAM Support Files This Appendix describes the concepts and methodology used to compile several key TEAM support files described in the main part of this Documentation: Concordances between sectoral frameworks (Section A.1) PPI adjustment factors (Section A.2) Total requirements coefficients (Section A.3) A.1 Concordances between Sectoral Frameworks A.1.1 Introduction TEAM s general purpose is to estimate the environmental impacts of a trade, or other economic event, specified as the change in domestic production levels by 4-digit NAICS sector. Although TEAM performs calculations in its native NAICS framework, it can accept as input an economic event specified in other economic sector classification systems and includes custom concordance tables to translate changes specified in these alternate systems into changes in the economic activity for each 4-digit NAICS sector. In addition to this run-time use of concordance tables, a number of data sources used in compiling the TEAM baseline datasets were provided in sector classifications systems other than NAICS and thus required the translation of the data into the NAICS framework. This included, for example, mapping SIC codes used to classify emissions into their corresponding 4-digit NAICS sectors, as discussed in Chapter 4. This Section describes the general concepts under in developing concordances used to translate data specified in other sector classification frameworks into NAICS. A.1.2 Trade or Economic Event Concordance Tables Standard Industrial Classification (SIC) Although not specifically included within the TEAM distribution disk, the SIC-NAICS concordance table is used at various steps of TEAM development (e.g., mapping the emissions baseline data as discussed in Chapter 4). More significantly for the current discussion, the SIC-NAICS concordance is used to develop concordances between NAICS and other classification systems, since several of these alternate classification systems are based on SIC classifications. It is therefore useful to discuss the SIC-to-NAICS mapping process here as an introduction to the development of concordances for other classification systems. Until 1997, U.S. economic statistics were primarily produced using the Standard Industrial Classification (SIC) system. The SIC system, which was originally devised in the 1930s and comprises 1,004 sectors, focused on manufacturing industries. The NAICS system was created in part to overcome the limitations of the SIC framework by enhancing definitions of information technologies, services, and other emerging industries NAICS, developed using a production-oriented conceptual framework, groups establishments into industries based on the activity in which they are primarily engaged. Establishments using similar raw material inputs, similar capital equipment, and similar labor are classified in the same industry. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 99

107 The U.S. Department of Commerce provides a bridge between the new NAICS system it adopted starting with the 1997 Economic Census, and the old 1987 SIC classification. This bridge, however, provides only a non-quantitative link between 4-digit SIC codes and their corresponding 4-digit NAICS sectors: it identifies the NAICS codes that relate to each SIC code. The bridge between SIC and NAICS codes identifies three types of relationships. These relationships determine how values assigned to a SIC sector are translated into the NAICS framework: 1. Mapping of a unique SIC code to a unique NAICS code (1-to-1 relationship). This is the simplest case. It simply involves directly assigning the value associated with a SIC sector to the corresponding NAICS sector. 2. Mapping of multiple SIC codes to 1 NAICS code (many-to-1 relationship). In this case, a 4-digit SIC sector corresponds to a unique NAICS sector, but that NAICS sector is also associated with other SIC sectors. The values attributed to the NAICS sector are then taken as the sum of the values for individual SIC sectors. 3. Mapping of 1 SIC code to multiple NAICS codes (1-to-many relationship). In this final case, each 4- digit SIC sector corresponds to more than one 4-digit NAICS code. The value associated with a given parent SIC sector must therefore be apportioned among the multiple child NAICS sectors using an appropriate weight. The mapping process is illustrated in Figure A-1 between SIC and 6-digit NAICS sectors. Note that the three scenarios listed above may be combined for a given NAICS or SIC sector. For instance, the NAICS sector in Figure C-1 is associated both with SIC Sector 0115 and SIC Sector 0119, and this later SIC sector is in turn associated with multiple NAICS sectors. TEAM uses the relative value of economic output to weight values translated from the SIC framework to the NAICS framework. Thus, when allocating a SIC value among multiple NAICS sectors, the value is apportioned based on the relative economic output associated with the NAICS sectors: Value NAICS i Output = ValueSIC j n Output i= 1 NAICS i NAICS i where: j = SIC sector i = NAICS sector n = number of NAICS sectors that map to SIC sector j. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 100

108 Figure A-1: Schematic Illustration of the Mapping Process for Translating Values from One Sector Framework to the 6-digit NAICS Sector Framework This use of the value of output as weights in the concordance table is consistent with accepted economic principles and with methodologies used in other economic models that require translation of values between sector classification frameworks. GTAP input-output tables, for examples, are developed by mapping data from one classification to the model framework using the sectors value of output or wholesale price indices as weights. The concordance provided in TEAM for run-time use is developed at the level of 4-digit NAICS and national. A state-level concordance was also developed for use in mapping emissions data, as described in Chapter 4, in which case the state-level concordance helped ensure that emissions would not be assigned to potential NAICS sectors that have no reported economic activity within a state. Bureau of Economic Analysis Sector Classifications The U.S. Department of Commerce Bureau of Economic Analysis (BEA) develops and releases benchmark input-output accounts of the U.S. economy every 5 years. The input-output accounts are prepared for the same years for which the Economic Census is collected (i.e., years ending in 2 and 7); however, the release date for the input-output accounts is delayed by several years beyond the release of the Economic Census for a given census year. BEA uses its own sector classification system to characterize U.S. economic activities. For the 1992 and earlier benchmark accounts, BEA based its industry classification system on the SIC system. This BEA92 framework included 498 industry sectors. For the 1997 accounts, BEA switched to NAICS as the underlying sectoral framework. The BEA97 framework defined 582 industry sectors. This same approach was retained for the 2002 benchmark accounts. The following sections describe the development of concordance tables between the BEA2002 framework and its 4-digit NAICS equivalent. This concordance table is used at run-time to translate trade or economic Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 101

109 events defined in the BEA2002 framework, for use in TEAM. Trade economic analyses prepared by the International Trade Commission are frequently prepared in an economic framework that is derived from, and maps most readily to, the BEA framework. Thus, the BEA-to-NAICS concordances are important translation frameworks for TEAM analyses. The BEA-to-NAICS concordance was also used in deriving NAICSdefined total requirements coefficients for use in TEAM, as described in Section A.3. The classification system used by BEA for the 2002 benchmark accounts was based on the NAICS classification system. Although BEA does not provide a quantitative mapping of the BEA framework to other sectoral classification systems, it does provide a list of NAICS sectors corresponding to each BEA sector. In many cases, the relationship between BEA and NAICS is one-to-one. In a process similar to that described above for translating values from the SIC framework into the 4-digit NAICS framework. The weights assigned to BEA values were determined directly as the relative value of output for related NAICS sectors. Thus, the BEA-to-NAICS weights can be calculated using: Value NAICS i = Value BEA j OutputNAICS i n OutputNAICSi i= II Table A-1 provides an excerpt of the BEA-to-NAICS concordance. As illustrated in the table, BEA sectors may map to more than one NAICS sectors (e.g., BEA sector 112A00); NAICS sectors may, in turn, be related to more than one BEA (e.g., NAICS sector 1121). In cases where a single BEA mapped to more than one NAICS sectors, we calculated weights based on the relative national value of shipments for the relevant sectors as described above. In general, however, no apportionment is necessary and the weight is equal to one (65 percent of possible BEA-NAICS combinations). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 102

110 Table A-1: Example BEA-to-NAICS Concordance. BEA Code BEA Sector Description NAICS Code Weight Dairy cattle and milk production A0 Cattle ranching and farming Poultry and egg production A00 Animal production, except cattle and poultry and eggs A00 Animal production, except cattle and poultry and eggs A00 Animal production, except cattle and poultry and eggs A00 Animal production, except cattle and poultry and eggs Logging A00 Forest nurseries, forest products, and timber tracts A00 Forest nurseries, forest products, and timber tracts Fishing Hunting and trapping Support activities for agriculture and forestry Support activities for agriculture and forestry Support activities for agriculture and forestry Oil and gas extraction Coal mining Iron ore mining A.1.3 Limitations of Concordance Tables The use of concordances to translate values provided in one sectoral classification framework to another involves a number of assumptions. First and foremost, the approach assumes that changes in economic activity for related sectors occur in proportion to the baseline value of output, since the output is used as weights to allocate values among multiple related sectors. The potential for uncertainty resulting from the use of concordances to define an economic event grows with the number of instances where more than one native sector maps to a single NAICS sector. For instance, if multiple SIC sectors map to the same NAICS sector, this NAICS sector may be allocated a disproportionately large value of output, while other related sectors would be allocated comparatively lower values. To a lesser extent, additional uncertainty is also introduced by instances where a native sector maps to multiple NAICS sectors. Thus, uncertainty introduced by the mapping process is generally minimized for native frameworks that follow levels of detail and underlying concepts that are similar to those used in NAICS. A.1.4 References Bureau of Economic Analysis (2008). Benchmark Input-Output Accounts of the United States, 2002, U.S. Department of Commerce, April. Chadha, R., and D. Pratap (2002). India. Chapter 11.I in GTAP V.5 Documentation, Center for Global Trade Analysis. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 103

111 Hertel, T. W., and M.E. Tsigas (1997). Structure of GTAP. Chapter 2 in Global Trade Analysis: Modeling and Applications, T. W. Hertel (ed.), Cambridge University Press. U.S. Census Bureau NAICS and 1987 SIC Correspondence Tables, Table 2. (Available online at A.2 PPI Adjustment TEAM uses Producer Price Index (PPI) data to adjust an economic event, specified in any given year between 1997 through 2007, to the TEAM baseline year of The support file covers the period of 1997 through 2007, which are the last ten years of data available from the U.S. Bureau of Labor Statistics (BLS). 105 BLS does not provide PPI data uniformly for all 4-digit NAICS sectors covered in TEAM (293 industries). PPI data are well populated in some industries such as the mining and manufacturing sectors, while no PPI data are reported by BLS in other economic sectors or are reported by NAICS only for later years. 106 For agricultural sectors, BLS does not use an industry classification system such as NAICS or SIC, but uses commodity classifications instead. Since most of the information reported by BLS is obtained through the systematic sampling of industries in mining and manufacturing sectors, the coverage of indexes for the service sectors of the economy is currently incomplete. 107 Several different approaches were used to develop the TEAM PPI support file for all 4-digit NAICS sectors, depending on the data available from BLS, as described below. Complete industry data reported: 41 NAICS sectors have complete PPI data for all 11 years ( ) and the BLS values were used directly. These sectors primarily cover manufacturing industries that contribute to most of the emissions captured in the TEAM framework. Industry data derived based on one related industry: PPI data for 192 NAICS sectors were derived based on one closely-related sector that serves as a proxy for the sector with missing data (e.g., Offices of Physicians was used as a proxy for Offices of Dentists -NAICS 6212). 108 Industry data derived based on several related industries: PPI data for 52 NAICS sectors were derived based on multiple proxies, using a weighted average calculated based on the national level of economic activity for each NAICS (e.g., Coal Mining, Metal Ore Mining, and Nonmetallic Mineral Mining and Quarrying were used as proxies for Support Activities for Mining -NAICS 2131). Industry data derived based on related commodities: PPI data for eight agricultural NAICS industries were derived based on commodity data, where most NAICS sectors map to multiple commodities (e.g., Citrus Fruits and Other Fruits and Berries serve as proxies for Fruit and Tree Nut 105 The data are available at: The PPI data for 2007 are reported as preliminary. 106 BLS changed its industry classification reporting framework from SIC to NAICS in All data prior to 2004 are reported by SIC sector. 107 BLS Handbook of Methods, Chapter 14, Producer Price Indexes. 108 Closely-related sectors are defined based on a higher-level NAICS sector within the same broad industry definition (e.g., 3-digit NAICS) or a relevant industry group provided by BLS. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 104

112 Farming -NAICS 1113). We estimated PPI data for those NAICS sectors as a simple average of PPIs for corresponding commodities. 109 The TEAM PPI support file obtained through the approaches outlined above covers all 293 NAICS sectors present in the TEAM economic baseline for the period of 1997 through The file includes both data values reported directly from BLS and values estimated by assuming similarity in how producer prices change over time in related sectors or commodities. While the process of filling in missing values introduces a degree of uncertainty in certain NAICS sectors, it is important to emphasize that NAICS sectors responsible for the majority of water and air emissions presented in TEAM, such as manufacturing sectors, mostly have complete PPI data reported by BLS. Sectors of the economy that do not have reported PPIs, and for which PPI values were therefore estimated for use in TEAM primarily represent various services, information sectors, and financial institutions that typically do not have high levels of emissions associated with them. A.3 Total Requirements Coefficients A.3.1 Developing the Input-Output Matrix The input-output total requirements data used in the TEAM model was developed using the 2002 Benchmark Input-Output Accounts published by BEA. The TEAM I-O Matrix provides the inputs of each industry (defined by its 4-digit NAICS code) that are directly or indirectly required to deliver a dollar of output to final users. The sum of values in each column of the matrix represents the changes in the industry sector output required to deliver a dollar of output from that industry to final users. The three input-output matrices were developed based on 2002 benchmark input-output tables published by the U.S. Department of Commerce Bureau of Economic Analysis (BEA). In particular: 2002 Supplementary Make, Use, and Direct Requirements Tables at the detailed level. The data set includes make, use, and direct requirements tables after redefinitions. Import Matrix from the 2002 Benchmark Input-Output Accounts. For each commodity, the importmatrix table shows the value of import of that same commodity used by each industry. The NERC-adjusted input-output matrix also draws on data from the Energy Information Administration (EIA) on the geographical distribution of electricity generation and revenue across the NERC regions. A.3.2 NAICS Sectoral Redefinition The BEA I-O tables use the BEA economic sectoral classification framework. The framework classifies economic activity into 426 industry sectors. Because TEAM uses 4-digit NAICS codes to classify economic sectors, the I-O files also need to be defined according to the NAICS framework. We used the concordance described in Section A.1.2 to map BEA sectors to their corresponding NAICS codes. Because of the fewer 4- digit NAICS sectors (less than 300) as compared to BEA sectors, mapping the I-O tables reduces the dimensions of the matrices. 109 We used a simple average due to the lack of a basis for generating weighed averages for agricultural commodities. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 105

113 A.3.3 Total Requirements TEAM uses the industry-by-industry total requirements coefficients to translate primary impacts in selected economic sectors, into total impacts across all linked economic sectors. The industry-by-industry coefficients represent, on a per-dollar basis, the industry output the economy generates in order to provide both directly and indirectly an industry s commodities to final users. The total requirements matrix was derived from Make and Use tables following the standard methodology described in the literature (BEA, 2006). This approach involves calculating the total requirements matrix as 1 matrix as ( I WB ) where: B: Direct input coefficients matrix in which entries in each column show the amount of a commodity used by an industry per dollar of output of that industry. It is a commodity-by-industry matrix. This matrix is derived from the Use Table. W: A matrix in which entries in each column show, for a given commodity, the proportion of the total output of that commodity produced in each industry. It is assumed that each commodity is produced by the various industries in fixed proportions. D is an industry-by-commodity matrix. W is also referred to as the market share matrix or transformation matrix. This matrix is derived from the Make Table. I: Identity matrix, i.e., a square matrix where the main diagonal is equal to one and all other cells contain zeros. The only adjustment made to the methodology above involved mapping BEA sectors to their 4-digit NAICS equivalents, as discussed in Section 2.1. The TEAM standard total requirements matrix was obtained using the following steps: Step 1: Convert BEA Make and Use tables to NAICS-defined Make and Use tables. The BEA sectors are mapped to their corresponding 4-digit NAICS sectors using the concordance table described in Section 2.1. Step 2: Calculate the direct requirements table from the Use table. The industry inputs in the Use table are divided by each industry s outputs to derive coefficients of the direct requirements table (B). Step 3a: Derive market share matrix from the Make table. The market share matrix provides the proportion of commodity output produced by each industry and is obtained by dividing each row of the make table by the total commodity output. Step 3b: Adjust market share for scrap. The market demand for scrap is eliminated by calculating the ratio of nonscrap output to industry output for each industry and applying these ratios to the market shares matrix to account for total industry output. Each row coefficient is divided by the nonscrap ratio for that industry. The resulting matrix is called the transformation matrix (W) Step 4: Create the industry-by-industry direct requirements matrix. The direct requirements matrix is obtained by multiplying the transformation matrix (W) times the industry-by-industry direct requirements (B). Step 5: Calculate the industry-by-industry total requirements matrix. The direct requirements matrix is subtracted from an identify matrix (I) and the resulting matrix is then inverted to provide the industry-by-industry total requirements matrix. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 106

114 A final step involved verifying that multiplying the total requirement table by the final uses by commodity equals the total commodity output for each industry. We verified that this equality was met across all NAICS sectors. A.3.4 Import-Adjusted Total Requirements BEA publishes several supplementary tables to the 2002 Benchmark I-O accounts, including import matrices showing the estimated use of imports by industries and final uses. In developing the TEAM I-O support files, we used the import matrix that reflects the reallocation of inputs ( after redefinitions ), consistent with other data sets used to derive the standard total requirements matrix, as described in Section A.3.3). The format of the import matrix is similar to that of the Use table, but only imported commodities are distributed across the rows of the table. The following steps were used in developing the import-adjusted total requirements matrix for use in TEAM: Step 1: Adjust the Use table to account for imports. The use table is adjusted by removing the share of inputs that are imported from each corresponding cell. The remaining values represent the share of inputs that is produced domestically. Step 2: Map adjusted use table to NAICS sectors. This import-adjusted use table is mapped to the NAICS sectors using the concordance described in Section 2.1. Steps 3: Derive the total requirements coefficients. Steps 2 through 5 of the approach described in Section 2.2 to derive the total requirements matrix from the standard Make and Use tables are applied to derive the import-adjusted total requirements matrix, but using the import-adjusted Use table as input. A.3.5 NERC-Adjusted Total Requirements The purpose of the NERC-adjusted total requirements matrix is to account for the variation in the profile of GHG emissions and other environmental impacts of electricity generation by North American Electric Reliability Corporation (NERC) region. 110 This regional treatment of electric power sector emissions is meant to support a better assessment of the GHG emissions impacts of electricity-consuming sectors, based on the specific regional profile of each sector s operations and the GHG emissions intensity of electricity produced within each NERC region. Further, it provides a similar improvement in understanding the differential burden from water and non-ghg air pollutant emissions associated with electricity consumption, based on the regional location of the electricity consuming sector. The NERC-adjusted input-output matrix is meant to complement an alternative set of economic and environmental baseline files that have been adjusted to define the electric power generation sector at the level of individual NERC regions. The following steps were used to derive the NERC-adjusted input output matrix for use in TEAM: 110 The ten NERC regions are: ASCC Alaska Systems Coordinating Council; ERCOT Electric Reliability Council of Texas; FRCC Florida Reliability Coordinating Council; HICC Hawaii Coordinating Council; MRO Midwest Reliability Organization; NPCC Northeast Power Coordinating Council; RFC Reliability First Corporation; SERC Southeastern Electric Reliability Council; SPP Southwest Power Pool; WECC Western Energy Coordinating Council. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 107

115 Step 1: Adjust the Use Table. To differentiate the impact of GHG and other pollutant emissions associated with purchased electricity by electricity-consuming sectors and by NERC region, we first allocated electricity use across NERC regions and subdivided the electric power sector by NERC region in the NAICS-based Use table: Allocate electricity across NERC regions: We first allocated electricity across ten NERC regions using the following methodology: To allocate electricity (commodity) used as an intermediate input 111 by electricity consuming industries across NERC regions, we developed allocation factors using the 2002 TEAM industry revenue data (state-level economic baseline file) 112 based on the state-to-nerc mapping (Table A-1). This step assumes that a given electricity-consuming sector purchases electricity from each NERC-specific electric power sector in proportion to the share of revenue from this consuming sector that is reported within the NERC region. Because the regional profile of economic activity differs by industry, these revenue-based allocation factors are different for each industry. Table A-1: State-to-NERC Region Mapping NERC-Specific NERC Region Electric Power Sector States ASCC 221A Alaska ERCOT 221E Texas FRCC 221F Florida HICC 221H Hawaii MRO 221M North Dakota, Minnesota, Nebraska, South Dakota, Iowa, Wisconsin NPCC RFC SERC 221N 221R 221S SPP 221P Kansas, Oklahoma WECC 221W Connecticut, Massachusetts, Maine, New Hampshire, New York, Rhode Island, Vermont Indiana, Kentucky, Ohio, West Virginia, Illinois, Michigan, Missouri, District of Columbia, Delaware, Maryland, New Jersey, Pennsylvania Alabama, Arkansas, Georgia, Louisiana, Mississippi, North Carolina, Tennessee, Virginia Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, Wyoming For the 26 industries with no state-level economic activity data (Table A-2), we assumed equal allocation of electricity across all NERC regions. This assumption is The intermediate inputs consumed by a given industry are the goods and services purchased by that industry for use in producing the industry s output. The value of intermediate inputs equals the industry s gross output (consisting of sales or receipts and other operating income, commodity taxes, and inventory change) less value added (consisting of compensation of employees, taxes on production and imports less subsidies, and gross operating surplus). These data are the 2002 state-level value of shipments and revenue from the Economic Census and Census of Agriculture, respectively. For information on these data and the methodology used to develop TEAM economic baseline data, see Chapter 3. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 108

116 needed to support developing the IO matrix. These sectors are not included in the TEAM economic baseline data set. Table A-2: Industries With No State-Level Revenue Data NAICS NAICS Definition Economic Value ($2002; Mln) 1131 Timber Tract Operations $ Forest Nurseries and Gathering of Forest Products $ Logging $ Hunting and Trapping $ Support Activities for Crop Production $ Support Activities for Animal Production $ Support Activities for Forestry $ Rail Transportation $ Postal Service $ Motion Picture and Video Industries $ Sound Recording Industries $ Wired Telecommunications Carriers $ Wireless Telecommunications Carriers (except Satellite) $ Cable and Other Program Distribution $ Depository Credit Intermediation $ Insurance Carriers $ Elementary and Secondary Schools $ Junior Colleges $1, Colleges, Universities, and Professional Schools $1, Religious Organizations $100.2 S001 Federal Electric Utilities and Other Federal Government Enterprises $135.2 S002 State and local government enterprises (passenger transit, electric utilities, and other) $1,504.7 S005 General Federal Defense Government Services $1,549.5 S006 General Federal Nondefense Government Services $659.4 S007 General State and Local Government Services $7,337.7 S008 Owner-occupied dwellings $0.0 To allocate electricity used by final consumers of electricity across NERC regions, we developed allocation factors using state-level electricity sales revenue from the EIA-861 database (Table A-3). This step assumes that a given final consumer of electricity purchases electricity from each NERC-specific electric power sector in proportion to this sector s share of electricity sales revenue reported within the NERC region. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 109

117 Table A-3: EIA-Based Allocation Factors NERC Region Allocation Factor ASCC ERCOT FRCC HICC MRO NPCC RFC SERC SPP WECC Subdivide the electric power sector into NERC regions: Once electricity (commodity) was allocated across NERC regions, we allocated commodities consumed by the electric power sector (industry) across NERC regions using the following methodology: To allocate electricity, we assumed that each NERC region consumes only its own electricity. To allocate oil and gas (represented by NAICS 2111: Oil & Gas Extraction) and coal (represented by NAICS 2121: Coal Mining) consumed by the electric power sector across NERC regions, we developed allocation factors using state-level fuel consumption data for the electric power sector from DOE s SEDS (Table A-4). This allocation is meant to account for differences in the mix of fossil fuels used to produce electricity across the NERC regions, and supports development of total requirements coefficients that account for the differences in consumption of these energy commodities for power production by NERC region. Table A-4: SEDS-Based Allocation Factors NERC Region Allocation Factor NAICS 2111: Oil & Gas Extraction ASCC ERCOT FRCC HICC MRO NPCC RFC SERC SPP WECC NAICS 2121: Coal Mining ASCC ERCOT FRCC Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 110

118 Table A-4: SEDS-Based Allocation Factors NERC Region Allocation Factor HICC MRO NPCC RFC SERC SPP WECC To allocate all other commodities consumed by the electric power sector across NERC regions, we developed allocation factors using total electricity output from the Use matrix already allocated across NERC regions (Table A-5). These allocation factors were developed in a way that ensured that the final output for the electric power sector (i.e., final industry output) is allocated across NERC regions in the same proportion as the final electricity output (i.e., final commodity output). This equality in proportional allocation of final industry and commodity output in the Use table is necessary to make sure that the final industry and commodity output in the Use table is the same as the final industry and commodity output, respectively, in the Make table once electricity is allocated across and the electric power sector are subdivided into NERC regions. Table A-5: Use Commodity Allocation Factors NERC Region Allocation Factor ASCC ERCOT FRCC HICC MRO NPCC RFC SERC SPP WECC Step 2: Adjust the Make Table. Once the above adjustments were made to the Use table, we allocated electricity use across NERC regions and subdivided the electric power sector into NERC regions in a way that ensured that the total industry and commodity output in the Make table is the same as the total industry and commodity output, respectively, in the Use table. We used the following methodology: Allocate electricity across NERC regions: To allocate electricity across NERC regions in the Make table, we applied allocation factors developed based on the total NERC-level electricity output from the Use table to each industry producing electricity (Table A-6). Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 111

119 Table A-6: Make Allocation Factors NERC Region Allocation Factor ASCC ERCOT FRCC HICC MRO NPCC RFC SERC SPP WECC Subdivide the electric power sector into NERC regions: We allocated electricity across NERC regions based on the assumption that the electric power sector in a given NERC region produces electricity only in that NERC region. We allocated other commodities produced by the electric power sector across NERC regions using allocation factors developed based on the total NERC-level electricity output from the Use table (Table A-5). Step 3: Derive the total requirement coefficients. The TEAM NERC-adjusted total requirements matrix using the NERC-adjusted Make and Use tables. As the result of these adjustments, instead of being reported for the total electric power sector, total requirements coefficients in the new TEAM NERC-adjusted total requirements coefficients file are reported for NERC-specific electric power sectors (denoted 221X where X is a letter that uniquely identifies the NERC region, generally the first letter in the Region s abbreviation (see Table A-1)). A.3.6 Final Data Sets and Considerations Three I-O support files were developed for use in TEAM: One file containing standard total requirements coefficients; One file containing import-adjusted total requirements coefficients; and One file containing NERC-adjusted total requirements coefficients. Each file provides the estimates of inputs directly and indirectly required from each of digit NAICS sectors (specified in the first column in the TEAM support file) to deliver a dollar of each 4-digit NAICS sector s output to final users (specified in the second column in the TEAM support file). Coefficients are given in the third column of the TEAM support file. Thus, each file describes a total requirements matrix 294 x 294 in dimensions, by providing in order: the row index, column index, and total requirements coefficient (86,436 coefficients). The standard and import-adjusted tables differ in that the import-adjusted total requirements coefficients reflect only the domestic contribution of each input sector required to produce one dollar of each output sector s production to final demand. The NERC-adjusted coefficients differ from the Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 112

120 standard coefficients by breaking out the electric power generation sector into ten NERC-specific electric power generation sectors, each with its distinct set of linkages to other economic sectors within the matrix. Table A-7 provides an excerpt of the TEAM I-O support files, showing for the same combinations of 4-digit NAICS sectors, the standard and import-adjusted industry-to-industry total requirements coefficients derived for use in TEAM. This example shows the contribution from several sectors (identified in the column ind1 ) required to produce a dollar of output of NAICS 3261: Plastics product manufacturing (identified in the column ind2 ), based on standard and import-adjusted total requirements coefficients. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 113

121 Table A-7: TEAM standard and import-adjusted industry-to-industry total requirements coefficients for selected NAICS sectors. Total Requirements Coefficients ind1 Sector description ind2 Sector description Standard Import-Adjusted 3261 Plastics product mfg 3261 Plastics product mfg Resin, syn rubber, & artificial syn 3261 Plastics product mfg fibers & filaments mfg 3251 Basic chemical mfg 3261 Plastics product mfg Management of companies & 3261 Plastics product mfg enterprises 3241 Petroleum & coal products mfg 3261 Plastics product mfg Oil and gas extraction 3261 Plastics product mfg Electric power generation, 3261 Plastics product mfg transmission, & distribution 3222 Converted paper product mfg 3261 Plastics product mfg Semiconductor & other electronic 3261 Plastics product mfg component mfg 3259 Other chemical product & 3261 Plastics product mfg preparation mfg 5221 Depository credit intermediation 3261 Plastics product mfg Lessors of nonfinancial intangible 3261 Plastics product mfg assets (exc copyrighted works) 3221 Pulp, paper, & paperboard mills 3261 Plastics product mfg Scientific research & development 3261 Plastics product mfg services 5311 Lessors of real estate 3261 Plastics product mfg Fiber, yarn, & thread mills 3261 Plastics product mfg General freight trucking 3261 Plastics product mfg Natural gas distribution 3261 Plastics product mfg Other professional, scientific, & 3261 Plastics product mfg technical services 5411 Legal services 3261 Plastics product mfg Services to buildings & dwellings 3261 Plastics product mfg Machine shops, turned product, & 3261 Plastics product mfg screw, nut, & bolt mfg 5413 Architectural, engineering, & 3261 Plastics product mfg related services 5222 Nondepository credit 3261 Plastics product mfg intermediation 4231 Mtr vehicle & parts & supplies merchant wholesalers 3261 Plastics product mfg In each instance, the Import-Adjusted Total Requirements Coefficient value is less than Unadjusted Total Requirements Coefficient, reflecting the fact that a fraction of the total requirements input of ind1 to the production of ind2 was supplied by imports. The observed numerical relationships between the unadjusted and import-adjusted values of the coefficients make intuitive sense. For example, the import-adjusted coefficient for NAICS 2111: Oil and gas extraction is approximately 36 percent of the unadjusted value, reflecting the high contribution of imports to domestic crude oil and gas consumption, while NAICS 2211: Electric power generation, transmission, & distribution shows a much higher contribution from domestic production, with its import-adjusted coefficient being approximately 87 percent of the unadjusted value. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 114

122 In processing the import-adjusted I-O data, we noted 41 4-digit NAICS commodities for which at least one import-adjusted direct requirement coefficient is negative. These commodities are listed in Table A-3, on the next page. In the table, we indicate the number of commodity-industry combinations for which the calculated direct requirement is negative and the minimum direct requirement coefficient. Upon investigation, we found that the anomalies shown in Table A-8 result from two distinct causes: (1) negative values present in the unadjusted Use Table, and (2) negative values resulting from the importadjustment. 113 Anomalies resulting from non-adjusted Use Table. Anomalies observed for agricultural sectors (1111, 1112, 1113, 1114, 1119, and 1142) are associated with negative values reported between these industries and NAICS 5241: Insurance Carriers. The anomalies are present even without import-adjustment, and in the direct requirement matrix published by BEA. BEA staff indicated that the negative value reflects the fact that insurance carriers reported losses in 2002, paying more in claims during the year than their revenue. No adjustment is warranted. Anomalies resulting from import-adjusted Use Table. Negative values reported for the other commodities appear as a result of the import-adjustment and are caused by the value of the imported commodity exceeding the amount reported as available for domestic use. For Audio & Video Equipment Manufacturing, for example, imports of the commodity to the retail trade sector (BEA sector 4A0000) is reported as $605.0 millions in the Import Table, as compared to $598.7 millions reported in the Use Table as available for domestic use by this sector. Since the retail trade sector maps to a large number of 4-digit NAICS sectors (NAICS 44-45), this anomaly gets distributed throughout the direct requirements table. 113 We discussed this discrepancy with BEA staff, who suggested that this result may occur for sectors that substantially increased inventories during the year, as compared to domestic consumption of the indicated input. In the example of Audio & Video Equipment Manufacturing, the reported change in private inventory is $771.4 millions. BEA staff suggested the possibility of adding the change in private inventories back into the amount of the commodity available for domestic use for those specific commodities where the anomaly is observed, and redistributing imports in proportion to their reported share. This adjustment was not applied to the TEAM data set, however, as it would result in an approach that is inconsistent across commodities. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 115

123 Table A-8: Commodities that have negative import-adjusted direct requirements. Commodity Commodityindustry Minimum NAICS Description pairs coefficient 3343 Audio and Video Equipment Manufacturing E Other Crop Farming E Vegetable and Melon Farming E Commercial and Service Industry Machinery Manufacturing E Electrical Equipment Manufacturing E Oilseed and Grain Farming E Fruit and Tree Nut Farming E Other Miscellaneous Manufacturing E Other Textile Product Mills E Dairy Product Manufacturing E Basic Chemical Manufacturing E Alumina and Aluminum Production and Processing E Cutlery and Handtool Manufacturing E Other General Purpose Machinery Manufacturing E Computer and Peripheral Equipment Manufacturing E Ventilation, Heating, Air-Conditioning, and Commercial Refrigeration E-09 Equipment Manufacturing 3141 Textile Furnishings Mills E Electric Lighting Equipment Manufacturing E Steel Product Manufacturing from Purchased Steel E Other Leather and Allied Product Manufacturing E Poultry and Egg Production E Communications Equipment Manufacturing E Fabric Mills E Greenhouse, Nursery, and Floriculture Production E Grain and Oilseed Milling E Iron and Steel Mills and Ferroalloy Manufacturing E Forest Nurseries and Gathering of Forest Products E Timber Tract Operations E Fruit and Vegetable Preserving and Specialty Food Manufacturing E Leather and Hide Tanning and Finishing E Veneer, Plywood, and Engineered Wood Product Manufacturing E Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments E-09 Manufacturing 3279 Other Nonmetallic Mineral Product Manufacturing E Inland Water Transportation Nonferrous Metal (except Aluminum) Production and Processing E Agriculture, Construction, and Mining Machinery Manufacturing E Metalworking Machinery Manufacturing E Manufacturing and Reproducing Magnetic and Optical Media E-06 The development of the import-adjusted total requirement coefficients embeds several assumptions regarding the composition of imported and exported goods. For example, in deriving the import matrix, BEA assumes that imports and U.S.-made intermediate goods are the same and have the same destinations (goods are interchangeable). BEA also assumes that each industry s use of imports for a specific commodity is Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 116

124 proportional to its total use of that commodity. In other words, each industry using a certain commodity uses it in the same proportion as imports-to-domestic supply of this commodity and no industry is more or less import-dependent than other industries using the same commodity. Additionally, exports and U.S.-consumed goods are the same and have the same input requirements, and imports do not embed U.S. exports. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 117

125 Appendix B Supporting Material for TEAM Economic Baseline Table B-1: Coverage of national and state-level TEAM baseline data sets by NAICS sector, as compared to national revenues reported in the Economic Census and Census of Agriculture. NAICS Total State Fraction of State Revenue Total National State Revenue / Revenue ($) Estimated Reported Revenue ($) National Revenue (%) 1111 $37,556,116, % 99.9% $37,540,988, % 1112 $13,050,809, % 99.3% $13,145,448, % 1113 $13,568,659, % 99.4% $13,489,154, % 1114 $15,065,592, % 100.0% $15,065,589, % 1119 $14,548,099, % 100.0% $14,548,102, % 1121 $65,388,442, % 100.0% $65,388,440, % 1122 $12,376,910, % 99.5% $12,337,959, % 1123 $24,380,493, % 99.5% $24,410,930, % 1124 $440,983, % 99.5% $445,366, % 1125 $1,137,789, % 99.0% $1,127,487, % 1129 $3,132,458, % 98.9% $3,146,893, % 1131 N/A N/A N/A N/A N/A 1132 N/A N/A N/A N/A N/A 1133 N/A N/A N/A N/A N/A 1141 N/A N/A N/A N/A N/A 1142 N/A N/A N/A N/A N/A 1151 N/A N/A N/A N/A N/A 1152 N/A N/A N/A N/A N/A 1153 N/A N/A N/A N/A N/A 2111 $113,088,133, % 99.7% $115,142,253, % 2121 $22,348,589, % 88.5% $20,238,422, % 2122 $6,198,635, % 61.6% $7,942,628, % 2123 $19,363,580, % 76.6% $19,363,580, % 2131 $21,908,498, % 99.5% $21,017,928, % 2211 $325,028,371, % 0.0% $325,028,371, % 2212 $66,515,186, % 0.0% $66,515,186, % 2213 $7,363,487, % 0.0% $7,363,487, % 2361 $264,748,802, % 100.0% $262,855,123, % 2362 $260,925,079, % 100.0% $258,994,632, % 2371 $78,911,588, % 97.9% $78,176,675, % 2372 $14,396,165, % 99.7% $14,461,720, % 2373 $83,355,185, % 100.0% $83,355,185, % 2379 $21,346,928, % 97.2% $21,476,272, % 2381 $115,652,635, % 99.1% $114,386,420, % 2382 $217,478,257, % 100.0% $217,478,257, % 2383 $87,148,122, % 99.2% $87,514,153, % 2389 $65,889,331, % 99.7% $66,141,097, % 3111 $27,833,242, % 98.0% $27,980,402, % 3112 $47,531,388, % 99.2% $47,032,806, % 3113 $23,306,910, % 78.2% $25,469,974, % 3114 $54,001,394, % 98.7% $53,380,011, % 3115 $66,990,836, % 98.0% $65,548,496, % 3116 $123,016,631, % 100.0% $122,174,387, % 3117 $9,663,051, % 86.8% $8,801,730, % 3118 $49,197,237, % 98.1% $48,482,723, % 3119 $57,668,393, % 98.8% $57,716,127, % 3121 $74,363,608, % 40.5% $65,153,490, % 3122 $32,009,112, % 66.6% $39,268,217, % 3131 $10,648,405, % 97.9% $10,592,767, % 3132 $22,482,011, % 98.5% $22,592,457, % 3133 $12,305,484, % 98.3% $12,283,289, % 3141 $22,404,002, % 100.0% $21,781,937, % Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 118

126 Table B-1: Coverage of national and state-level TEAM baseline data sets by NAICS sector, as compared to national revenues reported in the Economic Census and Census of Agriculture. NAICS Total State Fraction of State Revenue Total National State Revenue / Revenue ($) Estimated Reported Revenue ($) National Revenue (%) 3149 $9,738,368, % 99.2% $10,023,116, % 3151 $5,958,524, % 95.1% $6,083,170, % 3152 $35,882,728, % 97.4% $35,625,935, % 3159 $2,851,555, % 87.1% $2,911,492, % 3161 $2,080,861, % 75.3% $2,621,893, % 3162 $2,202,979, % 77.1% $2,229,756, % 3169 $1,719,619, % 74.2% $1,844,722, % 3211 $25,873,659, % 99.9% $25,761,530, % 3212 $20,179,832, % 99.6% $20,093,795, % 3219 $42,953,232, % 99.2% $42,438,918, % 3221 $70,136,460, % 95.4% $70,483,801, % 3222 $84,139,159, % 92.7% $82,167,687, % 3231 $96,921,488, % 97.5% $95,653,027, % 3241 $213,671,890, % 98.4% $216,019,846, % 3251 $110,127,600, % 100.0% $106,868,473, % 3252 $60,976,410, % 96.8% $61,321,973, % 3253 $18,692,321, % 95.5% $19,189,971, % 3254 $140,945,862, % 98.9% $140,664,173, % 3255 $27,869,672, % 95.5% $26,484,434, % 3256 $62,114,251, % 94.8% $62,058,618, % 3259 $39,463,462, % 96.7% $37,262,610, % 3261 $140,737,396, % 99.6% $140,096,885, % 3262 $33,043,407, % 99.6% $33,016,822, % 3271 $8,852,908, % 89.7% $8,414,667, % 3272 $22,420,845, % 99.4% $22,452,325, % 3273 $44,703,054, % 99.3% $44,365,822, % 3274 $4,423,964, % 86.1% $5,052,318, % 3279 $14,817,670, % 96.0% $14,582,726, % 3311 $47,307,988, % 92.3% $48,054,261, % 3312 $15,046,296, % 95.9% $15,125,046, % 3313 $27,550,177, % 94.1% $28,196,574, % 3314 $22,362,863, % 93.8% $21,842,498, % 3315 $27,153,764, % 95.5% $27,047,461, % 3321 $21,247,489, % 100.0% $21,453,238, % 3322 $10,724,274, % 98.5% $11,993,858, % 3323 $60,169,694, % 99.9% $59,681,466, % 3324 $22,991,325, % 98.8% $23,040,726, % 3325 $10,388,330, % 96.2% $10,225,196, % 3326 $9,061,047, % 99.5% $9,138,520, % 3327 $42,951,131, % 99.1% $42,966,030, % 3328 $18,740,465, % 100.0% $18,378,025, % 3329 $50,307,838, % 99.3% $50,774,384, % 3331 $47,751,748, % 97.5% $47,793,195, % 3332 $30,304,748, % 98.5% $29,430,033, % 3333 $21,050,344, % 99.4% $21,266,141, % 3334 $31,946,799, % 99.1% $32,289,537, % 3335 $25,482,323, % 99.6% $25,136,625, % 3336 $38,589,939, % 96.6% $38,410,210, % 3339 $57,404,984, % 99.8% $56,974,243, % 3341 $65,666,718, % 87.8% $73,112,637, % 3342 $66,977,545, % 94.5% $64,953,808, % 3343 $6,476,602, % 87.8% $8,823,531, % 3344 $114,166,350, % 92.0% $111,872,172, % 3345 $97,676,796, % 92.4% $93,291,968, % 3346 $7,368,691, % 90.7% $7,345,443, % Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 119

127 Table B-1: Coverage of national and state-level TEAM baseline data sets by NAICS sector, as compared to national revenues reported in the Economic Census and Census of Agriculture. NAICS Total State Fraction of State Revenue Total National State Revenue / Revenue ($) Estimated Reported Revenue ($) National Revenue (%) 3351 $13,247,900, % 67.6% $12,504,822, % 3352 $22,002,255, % 68.8% $22,692,213, % 3353 $31,859,649, % 98.4% $32,048,103, % 3359 $37,050,915, % 97.0% $36,874,218, % 3361 $244,219,851, % 80.6% $243,883,598, % 3362 $25,136,150, % 81.0% $24,135,396, % 3363 $221,332,483, % 90.4% $202,375,138, % 3364 $105,450,396, % 80.5% $125,245,578, % 3365 $8,257,476, % 77.4% $7,793,382, % 3366 $20,078,295, % 86.9% $21,244,316, % 3369 $13,251,060, % 70.2% $13,041,429, % 3371 $45,058,142, % 99.5% $45,160,903, % 3372 $23,247,773, % 99.3% $23,085,979, % 3379 $7,783,954, % 99.6% $7,530,566, % 3391 $62,032,013, % 99.1% $61,616,248, % 3399 $64,436,639, % 99.1% $64,126,283, % 4231 $601,381,670, % 94.3% $598,717,653, % 4232 $73,069,179, % 85.3% $71,428,120, % 4233 $114,808,794, % 95.6% $115,506,859, % 4234 $413,126,753, % 95.8% $406,445,349, % 4235 $121,359,462, % 56.9% $117,454,774, % 4236 $317,839,722, % 98.0% $322,377,337, % 4237 $84,083,512, % 90.7% $84,577,986, % 4238 $296,287,831, % 94.4% $297,936,999, % 4239 $148,021,093, % 46.3% $156,820,514, % 4241 $115,985,172, % 89.5% $114,410,461, % 4242 $384,646,379, % 96.2% $386,857,851, % 4243 $131,794,077, % 86.3% $118,346,385, % 4244 $512,928,661, % 98.0% $511,438,047, % 4245 $108,561,269, % 84.7% $103,402,280, % 4246 $109,451,366, % 95.4% $115,554,263, % 4247 $314,065,519, % 87.6% $321,246,536, % 4248 $89,397,299, % 85.5% $87,559,539, % 4249 $223,453,205, % 72.0% $221,514,304, % 4251 $474,494,142, % 94.8% $483,159,855, % 4411 $694,180,098, % 98.0% $693,840,253, % 4412 $46,878,910, % 98.0% $47,135,901, % 4413 $60,681,153, % 98.0% $60,764,008, % 4421 $50,192,368, % 98.0% $50,221,652, % 4422 $41,621,841, % 98.0% $41,592,558, % 4431 $82,246,809, % 97.9% $82,228,017, % 4441 $215,640,919, % 100.0% $215,640,919, % 4442 $30,919,932, % 100.0% $30,919,932, % 4451 $415,337,575, % 98.0% $415,613,872, % 4452 $13,159,803, % 96.5% $13,081,990, % 4453 $28,444,909, % 96.5% $28,246,426, % 4461 $177,947,091, % 100.0% $177,947,091, % 4471 $249,141,412, % 100.0% $249,141,412, % 4481 $120,191,738, % 98.2% $120,130,689, % 4482 $22,942,129, % 98.2% $22,955,111, % 4483 $24,800,199, % 98.2% $24,848,268, % 4511 $50,080,648, % 94.3% $50,116,683, % 4512 $23,131,556, % 94.4% $23,095,522, % 4521 $222,631,015, % 92.9% $220,742,882, % 4529 $222,244,938, % 92.8% $224,482,103, % Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 120

128 Table B-1: Coverage of national and state-level TEAM baseline data sets by NAICS sector, as compared to national revenues reported in the Economic Census and Census of Agriculture. NAICS Total State Fraction of State Revenue Total National State Revenue / Revenue ($) Estimated Reported Revenue ($) National Revenue (%) 4531 $6,647,629, % 93.8% $6,624,783, % 4532 $36,910,193, % 93.8% $36,618,807, % 4533 $7,874,312, % 95.9% $7,767,350, % 4539 $39,709,844, % 93.8% $39,800,802, % 4541 $120,459,113, % 97.3% $120,728,725, % 4542 $7,072,546, % 98.0% $7,048,506, % 4543 $45,333,306, % 97.3% $45,087,735, % 4811 $11,371,906, % 82.2% $12,156,568, % 4812 $7,842,345, % 85.0% $7,578,397, % 4821 N/A N/A N/A N/A N/A 4831 $20,617,180, % 92.7% $19,787,711, % 4832 $2,630,270, % 84.9% $3,543,622, % 4841 $109,338,335, % 91.4% $110,239,101, % 4842 $53,870,322, % 91.4% $53,979,668, % 4851 $3,022,365, % 85.4% $2,808,395, % 4852 $1,421,506, % 83.9% $1,530,944, % 4853 $4,779,847, % 84.2% $4,248,494, % 4854 $6,273,679, % 93.2% $5,928,085, % 4855 $1,746,209, % 93.8% $1,761,506, % 4859 $2,633,301, % 86.5% $2,572,494, % 4861 $2,291,036, % 64.9% $3,401,854, % 4862 $14,824,718, % 85.4% $14,797,371, % 4869 $4,480,861, % 36.1% $3,832,194, % 4871 $761,309, % 45.9% $636,371, % 4872 $1,062,427, % 79.6% $1,002,203, % 4879 $177,180, % 89.3% $220,405, % 4881 $12,321,784, % 90.7% $12,181,244, % 4882 $2,011,029, % 78.6% $1,821,557, % 4883 $9,525,082, % 82.4% $8,811,911, % 4884 $4,024,907, % 93.8% $4,003,111, % 4885 $28,190,998, % 94.3% $27,656,336, % 4889 $2,296,603, % 66.7% $2,939,972, % 4911 N/A N/A N/A N/A N/A 4921 $53,314,921, % 89.6% $54,820,521, % 4922 $3,491,517, % 88.2% $3,344,348, % 4931 $17,558,707, % 93.0% $16,547,657, % 5111 $138,544,060, % 99.0% $138,710,521, % 5112 $103,672,308, % 99.0% $103,505,848, % 5121 N/A N/A N/A $62,926,611, % 5122 N/A N/A N/A $15,323,757, % 5151 $51,794,620, % 82.0% $48,589,052, % 5152 $22,167,497, % 78.0% $25,373,066, % 5161 $6,315,201, % 100.0% $6,363,468, % 5171 N/A N/A N/A $237,697,299, % 5172 N/A N/A N/A $99,192,758, % 5173 $9,595,213, % 100.0% $9,716,887, % 5174 $5,606,842, % 100.0% $5,748,139, % 5175 N/A N/A N/A $57,708,708, % 5179 $1,440,765, % 100.0% $1,580,752, % 5181 $21,418,640, % 100.0% $21,418,640, % 5182 $53,089,145, % 100.0% $53,089,145, % 5191 $4,862,370, % 100.0% $4,901,305, % 5211 $28,909,454, % 100.0% $28,909,454, % 5221 N/A N/A N/A $604,573,768, % 5222 $338,434,317, % 100.0% $396,893,716, % Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 121

129 Table B-1: Coverage of national and state-level TEAM baseline data sets by NAICS sector, as compared to national revenues reported in the Economic Census and Census of Agriculture. NAICS Total State Fraction of State Revenue Total National State Revenue / Revenue ($) Estimated Reported Revenue ($) National Revenue (%) 5223 $50,132,494, % 100.0% $54,246,303, % 5231 $207,561,648, % 89.9% $212,236,041, % 5232 $2,731,779, % 55.7% $3,051,000, % 5239 $57,868,357, % 18.5% $100,988,114, % 5241 N/A N/A N/A $1,273,345,560, % 5242 $98,872,058, % 100.0% $106,737,257, % 5251 N/A N/A N/A N/A N/A 5259 $14,720,757, % 100.0% $22,873,655, % 5311 $116,239,467, % 100.0% $116,240,519, % 5312 $61,393,577, % 97.6% $63,381,021, % 5313 $44,627,296, % 93.3% $43,941,574, % 5321 $37,273,305, % 93.1% $35,778,730, % 5322 $20,775,148, % 95.0% $20,700,029, % 5323 $3,479,664, % 91.5% $3,386,786, % 5324 $36,123,423, % 94.0% $35,241,606, % 5331 $10,870,335, % 84.9% $16,917,441, % 5411 $182,098,033, % 100.0% $182,098,002, % 5412 $84,072,025, % 100.0% $84,072,026, % 5413 $161,886,554, % 100.0% $161,835,036, % 5414 $17,074,885, % 100.0% $17,074,885, % 5415 $173,414,779, % 100.0% $173,414,189, % 5416 $105,485,555, % 100.0% $105,451,814, % 5417 $64,728,712, % 98.5% $64,481,193, % 5418 $56,680,662, % 100.0% $56,680,662, % 5419 $41,445,711, % 98.5% $41,693,231, % 5511 $107,062,535, % 100.0% $107,064,264, % 5611 $32,116,309, % 100.0% $32,080,759, % 5612 $13,121,331, % 97.4% $12,956,879, % 5613 $128,649,583, % 100.0% $128,661,919, % 5614 $43,978,782, % 100.0% $43,978,782, % 5615 $25,556,402, % 100.0% $25,535,314, % 5616 $31,375,440, % 100.0% $31,375,440, % 5617 $75,386,448, % 100.0% $75,315,804, % 5619 $31,281,243, % 97.4% $31,363,407, % 5621 $28,155,620, % 97.9% $28,205,473, % 5622 $10,893,666, % 98.6% $10,833,837, % 5629 $12,339,879, % 97.7% $12,269,966, % 6111 N/A N/A N/A N/A N/A 6112 N/A N/A N/A N/A N/A 6113 N/A N/A N/A N/A N/A 6114 $7,821,529, % 98.2% $7,717,082, % 6115 $7,404,071, % 99.4% $7,451,159, % 6116 $9,793,180, % 97.6% $9,783,370, % 6117 $5,684,189, % 97.2% $5,739,096, % 6211 $248,824,594, % 100.0% $248,824,594, % 6212 $71,102,922, % 100.0% $71,102,922, % 6213 $37,127,519, % 100.0% $37,127,519, % 6214 $54,818,719, % 100.0% $54,818,719, % 6215 $28,409,347, % 100.0% $28,409,347, % 6216 $30,386,230, % 100.0% $30,386,230, % 6219 $17,949,197, % 100.0% $17,949,197, % 6221 $469,518,069, % 98.4% $469,726,928, % 6222 $13,475,480, % 87.8% $13,626,730, % 6223 $17,041,282, % 89.7% $16,759,183, % 6231 $74,116,741, % 100.0% $74,116,741, % Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 122

130 Table B-1: Coverage of national and state-level TEAM baseline data sets by NAICS sector, as compared to national revenues reported in the Economic Census and Census of Agriculture. NAICS Total State Fraction of State Revenue Total National State Revenue / Revenue ($) Estimated Reported Revenue ($) National Revenue (%) 6232 $19,315,572, % 100.0% $19,317,422, % 6233 $26,099,031, % 100.0% $26,099,031, % 6239 $7,601,070, % 100.0% $7,580,372, % 6241 $44,641,436, % 100.0% $44,645,659, % 6242 $14,003,274, % 100.0% $14,005,774, % 6243 $11,031,411, % 100.0% $11,031,411, % 6244 $21,771,955, % 100.0% $21,771,955, % 7111 $11,066,874, % 95.9% $10,863,631, % 7112 $22,579,976, % 97.5% $22,313,416, % 7113 $11,176,508, % 96.0% $12,168,551, % 7114 $3,735,366, % 95.8% $3,602,288, % 7115 $9,343,084, % 99.5% $9,337,795, % 7121 $8,732,259, % 96.3% $8,607,959, % 7131 $9,564,813, % 98.5% $9,443,200, % 7132 $19,798,631, % 84.3% $18,892,878, % 7139 $45,910,837, % 80.0% $46,674,391, % 7211 $123,968,879, % 95.9% $123,899,931, % 7212 $3,409,499, % 95.8% $3,466,917, % 7213 $719,830, % 95.9% $731,362, % 7221 $144,571,405, % 97.7% $144,649,964, % 7222 $135,421,184, % 97.7% $135,324,135, % 7223 $26,524,822, % 100.0% $26,524,822, % 7224 $14,883,096, % 97.7% $14,901,587, % 8111 $75,219,140, % 100.0% $75,219,140, % 8112 $14,985,124, % 100.0% $14,982,884, % 8113 $19,486,464, % 100.0% $19,485,118, % 8114 $8,619,741, % 100.0% $8,618,604, % 8121 $20,220,607, % 100.0% $20,217,426, % 8122 $14,280,804, % 100.0% $14,279,878, % 8123 $20,443,777, % 100.0% $20,443,881, % 8129 $17,280,485, % 100.0% $17,279,219, % 8131 N/A N/A N/A N/A N/A 8132 $46,275,707, % 100.0% $46,275,707, % 8133 $12,058,846, % 100.0% $12,058,846, % 8134 $14,677,629, % 100.0% $14,679,408, % 8139 $43,478,925, % 100.0% $43,509,350, % 8141 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A TOTAL $19,095,297,580, % 92.8% $21,561,625,353, % Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 123

131 Appendix C Supporting Material for TEAM Emissions Baseline C.1 Area Source Air Emissions Table C-1: SCC Codes Mapping to a Large Number of NAICS Codes SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Number of Mapped NAICS Stationary Source Fuel Total Area Source Fuel Total: Boilers and IC Distillate Oil Combustion Combustion Engines 286 Stationary Source Fuel Total Area Source Fuel Total: Boilers and IC Natural Gas Combustion Combustion Engines 286 Stationary Source Fuel Total Area Source Fuel Combustion Combustion Liquefied Petroleum Gas (LPG) Total: All Boiler Types 286 Waste Disposal, On-site Incineration Treatment, and Recovery All Categories Total 286 Stationary Source Fuel Total Area Source Fuel Combustion Combustion Residual Oil Total: All Boiler Types 285 Stationary Source Fuel Total Area Source Fuel Combustion Combustion Anthracite Coal Total: All Boiler Types 284 Stationary Source Fuel Total Area Source Fuel Combustion Combustion Distillate Oil All IC Engine Types Solvent Utilization All Solvent User Categories All Processes Total: All Solvent Types 284 Stationary Source Fuel Total Area Source Fuel Combustion Combustion Kerosene Total: All Heater Types 281 Stationary Source Fuel Total Area Source Fuel Combustion Combustion Distillate Oil All Boiler Types 279 Stationary Source Fuel Total Area Source Fuel Combustion Combustion Natural Gas All Boiler Types 279 Stationary Source Fuel Total Area Source Fuel Combustion Combustion Wood Total: All Boiler Types 279 Stationary Source Fuel Combustion Commercial/Institutional Anthracite Coal Total: All Boiler Types 151 Stationary Source Fuel Combustion Commercial/Institutional Bituminous/Subbituminous Coal Total: All Boiler Types 151 Stationary Source Fuel Total: Boilers and IC Commercial/Institutional Distillate Oil Combustion Engines 151 Stationary Source Fuel Combustion Commercial/Institutional Residual Oil Total: All Boiler Types 151 Stationary Source Fuel Total: Boilers and IC Commercial/Institutional Natural Gas Combustion Engines 151 Stationary Source Fuel Combustion Commercial/Institutional Liquefied Petroleum Gas (LPG) Total: All Combustor Types 151 Stationary Source Fuel Combustion Commercial/Institutional Wood Total: All Boiler Types 151 Stationary Source Fuel POTW Digester Gas-fired Commercial/Institutional Process gas Combustion Boilers 151 Stationary Source Fuel Combustion Commercial/Institutional Kerosene Total: All Combustor Types 151 Waste Disposal, On-site Incineration Treatment, and Recovery Commercial/Institutional Total 151 Stationary Source Fuel Combustion Commercial/Institutional Kerosene All Boiler Types 147 Stationary Source Fuel Combustion Commercial/Institutional Liquefied Petroleum Gas (LPG) All Boiler Types Solvent Utilization Miscellaneous Nonindustrial: Commercial Processes Tank/Drum Cleaning: All Total: All Solvent Types Solvent Utilization Miscellaneous Nonindustrial: Commercial Processes Solvent Reclamation: All Total: All Solvent Types Miscellaneous Area Catastrophic/Accidental All Catastrophic/Accidental Total 96 Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 124

132 Table C-1: SCC Codes Mapping to a Large Number of NAICS Codes SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Number of Mapped NAICS Sources Releases Releases Stationary Source Fuel Combustion Industrial Anthracite Coal Total: All Boiler Types 86 Stationary Source Fuel Combustion Industrial Bituminous/Subbituminous Coal Total: All Boiler Types 86 Stationary Source Fuel Total: Boilers and IC Industrial Distillate Oil Combustion Engines 86 Stationary Source Fuel Combustion Industrial Residual Oil Total: All Boiler Types 86 Stationary Source Fuel Total: Boilers and IC Industrial Natural Gas Combustion Engines 86 Stationary Source Fuel Combustion Industrial Natural Gas All Boiler Types 86 Stationary Source Fuel Combustion Industrial Natural Gas All IC Engine Types 86 Stationary Source Fuel Combustion Industrial Liquefied Petroleum Gas (LPG) Total: All Boiler Types 86 Stationary Source Fuel Combustion Industrial Wood Total: All Boiler Types 86 Stationary Source Fuel Combustion Industrial Kerosene Total: All Boiler Types 86 Stationary Source Fuel Combustion Industrial Waste oil Total Industrial Processes Industrial Processes: NEC Industrial Processes: NEC Total Industrial Processes Industrial Refrigeration Refrigerant Losses All Processes Solvent Utilization Surface Coating Industrial Maintenance Coatings Total: All Solvent Types 86 Waste Disposal, On-site Incineration Treatment, and Recovery Industrial Total 86 Miscellaneous Area Catastrophic/Accidental Sources Releases Industrial Accidents Total 84 Stationary Source Fuel Combustion Industrial Process Gas Total: All Boiler Types 82 Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 125

133 Table C-2: Household Emissions SCC SCC Level I SCC Level II SCC Level III SCC Level IV Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Stationary Source Fuel Combustion Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Waste Disposal, Treatment, and Recovery Miscellaneous Area Sources Emissions (tons) Residential Anthracite Coal Total: All Combustor Types 60,470 Residential Bituminous/Subbituminous Coal Total: All Combustor Types 84,449 Residential Distillate Oil Total: All Combustor Types 228,587 Residential Residual Oil Total: All Combustor Types 3 Residential Natural Gas Total: All Combustor Types 321,032 Residential Natural Gas Residential Furnaces 44,289 Residential Residential Liquefied Petroleum Gas (LPG) Wood Total: All Combustor Types 42,720 Total: Woodstoves and Fireplaces Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 126 1,547,495 Residential Wood Fireplaces: General 1,451,163 Residential Residential Residential Wood Wood Wood Fireplaces: Insert; non-epa certified Fireplaces: Insert; EPA certified; non-catalytic Fireplaces: Insert; EPA certified; catalytic 574,543 29,146 11,143 Residential Wood Woodstoves: General 1,236,245 Residential Wood Catalytic Woodstoves: General 40,140 Residential Residential Residential Residential Residential Wood Wood Wood Wood Wood Non-catalytic Woodstoves: EPA certified Non-catalytic Woodstoves: Non-EPA certified Non-catalytic Woodstoves: Low Emitting Non-catalytic Woodstoves: Pellet Fired Outdoor Wood Burning Equipment 151, , ,778 2,384 86,771 Residential Firelog Total: All Combustor Types 2,005 Residential Kerosene Total: All Heater Types 17,689 Miscellaneous Nonindustrial: Consumer Miscellaneous Nonindustrial: Consumer Miscellaneous Nonindustrial: Consumer Miscellaneous Nonindustrial: Consumer Miscellaneous Nonindustrial: Consumer Miscellaneous Nonindustrial: Consumer Miscellaneous Nonindustrial: Consumer All Products/Processes Total: All Solvent Types 196,372 Personal Care Products Total: All Solvent Types 66,700 Household Products Total: All Solvent Types 78,565 Automotive Aftermarket Products Total: All Solvent Types 144,455 Adhesives and Sealants Total: All Solvent Types 27,421 Pesticide Application Total: All Solvent Types 29,136 Miscellaneous Products: NEC Total: All Solvent Types 28,196 On-site Incineration Residential Total 6,206 Other Combustion Charcoal Grilling - Residential (see xxx Total 2,848

134 Table C-3: NEI Emissions Excluded from the TEAM Emissions Baseline Dataset SCC Level SCC SCC Level II SCC Level III SCC Level IV Frequency I Miscellaneous Area Sources Miscellaneous Area Sources Miscellaneous Area Sources Miscellaneous Area Sources Miscellaneous Area Sources Miscellaneous Area Sources Storage and Transport Miscellaneous Area Sources Storage and Transport Industrial Processes Industrial Processes Solvent Utilization Solvent Utilization Storage and Transport Solvent Utilization Solvent Utilization Solvent Utilization Industrial Processes Solvent Utilization Industrial Processes Solvent Utilization Industrial Processes Solvent Utilization Solvent Utilization Industrial Processes Solvent Utilization Other Combustion Other Combustion Other Combustion Other Combustion Other Combustion Other Combustion Petroleum and Petroleum Product Transport Other Combustion Bulk Materials Transport Chemical Manufacturing: SIC 28 Food and Kindred Products: SIC 20 Surface Coating Emissions (tons) % of NEI Forest Wildfires * Total 1,735 20,394,643 30% Prescribed Burning for Forest Management * Managed Burning, Slash (Logging Debris) * Prescribed Burning for Forest Management * Total (1) 1,260 2,742,373 4% Total ,028 1% Natural (1) ,940 0% Structure Fires * Total ,682 0% Motor Vehicle Fires * Total ,274 0% Pipeline Gasoline 90 10,811 0% Prescribed Burning of Rangeland * Rail Car * Process Emissions from Pharmaceutical Manuf (NAPAP Commercial Cooking - Charbroiling Other Special Purpose Coatings Surface Coating Aircraft: SIC 372 Petroleum and Petroleum Product Transport Surface Coating Paint Strippers Rail Tank Car * Large Appliances: SIC 363 Chemical Strippers Surface Coating Marine: SIC 373 Food and Kindred Products: SIC 20 Surface Coating Food and Kindred Products: SIC 20 Commercial Cooking - Frying Factory Finished Wood: SIC 2426 thru 242 Commercial Cooking - Charbroiling Surface Coating Metal Cans: SIC 341 Chemical Manufacturing: SIC 28 Miscellaneous Non-industrial: Commercial Surface Coating Chemical Manufacturing: SIC 28 Plastics Production Pesticide Application: Agricultural Electronic and Other Electrical: SIC Plastics Production Surface Coating Railroad: SIC 374 Total 252 9,238 0% Total: All Products 6 3,051 0% Total 14 2,176 0% Under-fired Charbroiling Total: All Solvent Types Total: All Solvent Types Total: All Products Total: All Solvent Types Application, Degradation, and Coating Removal Step Total: All Solvent Types Flat Griddle Frying Total: All Solvent Types Conveyorized Charbroiling Total: All Solvent Types Reactor (Polyurethane) 132 1,895 0% 96 1,796 0% 16 1,280 0% 11 1,042 0% % % % % % % % % All Processes % Total: All Solvent Types Foam Production - General Process Total: All Solvent Types % % % Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 127

135 Table C-3: NEI Emissions Excluded from the TEAM Emissions Baseline Dataset SCC Level SCC SCC Level II SCC Level III SCC Level IV Frequency I Industrial Processes Industrial Processes Solvent Utilization Waste Disposal, Treatment, and Recovery Industrial Processes Industrial Processes Industrial Processes Industrial Processes Storage and Transport Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Solvent Utilization Primary Metal Production: SIC 33 Food and Kindred Products: SIC 20 Surface Coating Wastewater Treatment Food and Kindred Products: SIC 20 Petroleum Refining: SIC 29 Food and Kindred Products: SIC 20 Oil and Gas Production: SIC 13 Petroleum and Petroleum Product Transport Degreasing Degreasing Degreasing Rubber/Plastics Degreasing Degreasing Surface Coating Degreasing Degreasing Degreasing Degreasing Emissions (tons) % of NEI All Processes Total % Commercial Cooking - Frying Motor Vehicles: SIC 371 Clamshell Griddle Frying Total: All Solvent Types % % Public Owned Total Processed % Commercial Cooking - Frying Deep Fat Frying % All Processes Total % Grain Mill Products Total 2 9 0% Natural Gas Marine Vessel Primary Metal Industries (SIC 33): Open Top Degrea Fabricated Metal Products (SIC 34): Open Top Degre Electronic and Other Elec. (SIC 36): Open Top Degr All Processes Primary Metal Industries (SIC 33): Cold Cleaning Instruments and Related Products (SIC 38): Open To Machinery and Equipment: SIC 35 Industrial Machinery and Equipment (SIC 35): Open Fabricated Metal Products (SIC 34): Cold Cleaning Transportation Equipment (SIC 37): Open Top Degrea Electronic and Other Elec. (SIC 36): Cold Cleaning Surface Coating Paper: SIC 26 Degreasing Degreasing Surface Coating Industrial Machinery and Equipment (SIC 35): Cold Instruments and Related Products (SIC 38): Cold Cl Miscellaneous Finished Metals: SIC Total: All Processes Total: All Products Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types Total: All Solvent Types % 1 5 0% 2 4 0% 1 4 0% 1 3 0% 9 3 0% 2 3 0% 1 2 0% % 1 1 0% 1 1 0% 1 1 0% 1 1 0% 8 0 0% 1 0 0% 1 0 0% 8 0 0% Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 128

136 Table C-3: NEI Emissions Excluded from the TEAM Emissions Baseline Dataset SCC Level SCC SCC Level II SCC Level III SCC Level IV Frequency I Industrial Processes Petroleum Refining: SIC ( Asphalt Paving/Roofing Materials Non-recycling Related Emissions Coating, Engraving, and Allied Services Coating, Engraving, and Allied Services Coating, Engraving, and Allied Services Recycling Related Emissions Emissions (tons) % of NEI Total % Miscellaneous Fluorescent Area Sources Lamp Breakage Total 8 0 0% Industrial Fabricated Processes Metals: SIC 34 Electroplating 8 0 0% Industrial Fabricated Plating: Metal Processes Metals: SIC 34 Deposition 8 0 0% Industrial Fabricated Processes Metals: SIC 34 Anodizing 6 0 0% Miscellaneous Fluorescent Area Sources Lamp Breakage Total 7 0 0% Total Lost 23,914,091 35% Total Reported by NEI 67,783, % *: SCC category is excluded completely from the TEAM baseline dataset because it has no corresponding NAICS sector (e.g., outside the scope of the 2002 Economic Census). (1): According to EPA NEI staff, SCC Categories : Miscellaneous Area Sources: Other Combustion: Prescribed Burning for Forest Management: Natural is not a subcomponent of : Miscellaneous Area Sources: Other Combustion: Prescribed Burning for Forest Management: Total as emissions may be reported for either individual sub-processes or as a total estimate. These and other similar SCC Level IV categories do not overlap. C.2 Mobile Source Air Emissions Table C-4: SCC Sectors Mapping to a Large Number of NAICS Sectors SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Number of Mapped NAICS Mobile Sources Off-highway Vehicle Gasoline, 2-Stroke Commercial Equipment Total Mobile Sources Off-highway Vehicle Gasoline, 2-Stroke Commercial Equipment Generator Sets Mobile Sources Off-highway Vehicle Gasoline, 2-Stroke Commercial Equipment Pumps Mobile Sources Off-highway Vehicle Gasoline, 2-Stroke Commercial Equipment Air Compressors Mobile Sources Off-highway Vehicle Gasoline, 2-Stroke Commercial Equipment Hydro-power Units Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Commercial Equipment Total Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Commercial Equipment Generator Sets Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Commercial Equipment Pumps Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Commercial Equipment Air Compressors Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Commercial Equipment Welders Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Commercial Equipment Pressure Washers Mobile Sources Off-highway Vehicle Commercial Equipment Hydro-power Units 156 Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 129

137 Table C-4: SCC Sectors Mapping to a Large Number of NAICS Sectors SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Number of Mapped NAICS Gasoline, 4-Stroke Mobile Sources LPG Commercial Equipment Generator Sets Mobile Sources LPG Commercial Equipment Pumps Mobile Sources LPG Commercial Equipment Air Compressors Mobile Sources LPG Commercial Equipment Welders Mobile Sources LPG Commercial Equipment Pressure Washers Mobile Sources LPG Commercial Equipment Hydro-power Units Mobile Sources CNG Commercial Equipment All Mobile Sources CNG Commercial Equipment Generator Sets Mobile Sources CNG Commercial Equipment Pumps Mobile Sources CNG Commercial Equipment Air Compressors Mobile Sources CNG Commercial Equipment Gas Compressors Mobile Sources CNG Commercial Equipment Hydro-power Units Mobile Sources Off-highway Vehicle Diesel Commercial Equipment Total Mobile Sources Off-highway Vehicle Diesel Commercial Equipment Generator Sets Mobile Sources Off-highway Vehicle Diesel Commercial Equipment Pumps Mobile Sources Off-highway Vehicle Diesel Commercial Equipment Air Compressors Mobile Sources Off-highway Vehicle Diesel Commercial Equipment Gas Compressors Mobile Sources Off-highway Vehicle Diesel Commercial Equipment Welders Mobile Sources Off-highway Vehicle Diesel Commercial Equipment Pressure Washers Mobile Sources Off-highway Vehicle Diesel Commercial Equipment Hydro-power Units Mobile Sources Off-highway Vehicle Gasoline, 2-Stroke Industrial Equipment Total Mobile Sources Off-highway Vehicle Gasoline, 2-Stroke Industrial Equipment Sweepers/Scrubbers Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Off-highway Vehicle Gasoline, 2-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Industrial Equipment Other General Industrial Equipment Industrial Equipment Total * 86 Industrial Equipment Aerial Lifts 86 Industrial Equipment Forklifts 86 Industrial Equipment Sweepers/Scrubbers 86 Industrial Equipment Industrial Equipment Other General Industrial Equipment Other Material Handling Equipment Industrial Equipment AC\Refrigeration 86 Industrial Equipment Terminal Tractors Mobile Sources LPG Industrial Equipment Aerial Lifts Mobile Sources LPG Industrial Equipment Forklifts Mobile Sources LPG Industrial Equipment Sweepers/Scrubbers Mobile Sources LPG Industrial Equipment Other General Industrial Equipment Mobile Sources LPG Industrial Equipment Other Material Handling Equipment Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 130

138 Table C-4: SCC Sectors Mapping to a Large Number of NAICS Sectors SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Number of Mapped NAICS Mobile Sources LPG Industrial Equipment Terminal Tractors Mobile Sources CNG Industrial Equipment All Mobile Sources CNG Industrial Equipment Forklifts Mobile Sources CNG Industrial Equipment Sweepers/Scrubbers Mobile Sources CNG Industrial Equipment Other General Industrial Equipment Mobile Sources CNG Industrial Equipment AC\Refrigeration Mobile Sources CNG Industrial Equipment Terminal Tractors Mobile Sources Off-highway Vehicle Diesel Industrial Equipment Total * Mobile Sources Off-highway Vehicle Diesel Industrial Equipment Aerial Lifts Mobile Sources Off-highway Vehicle Diesel Industrial Equipment Forklifts Mobile Sources Off-highway Vehicle Diesel Industrial Equipment Sweepers/Scrubbers Mobile Sources Off-highway Vehicle Diesel Industrial Equipment Other General Industrial Equipment Mobile Sources Off-highway Vehicle Diesel Industrial Equipment Other Material Handling Equipment Mobile Sources Off-highway Vehicle Diesel Industrial Equipment AC\Refrigeration Mobile Sources Off-highway Vehicle Diesel Industrial Equipment Terminal Tractors 86 * The 'Total" category includes all other equipment that could not be allocated to any particular source. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 131

139 Table C-5: SCC Categories Associated with the Household/Residential Off-Highway Emissions SCC Code SCC Level I SCC Level II SCC Level III SCC Level IV Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Mobile Sources Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 2-Stroke Off-highway Vehicle Gasoline, 2-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 2-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 2-Stroke Off-highway Vehicle Gasoline, 4-Stroke Off-highway Vehicle Gasoline, 2-Stroke Off-highway Vehicle Gasoline, 4-Stroke Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Equipment Lawn and Garden Tractors (Residential) Emissions (Tons) 2,324,280 Lawn Mowers (Residential) 849,809 Rear Engine Riding Mowers (Residential) Trimmers/Edgers/Brush Cutters (Residential) Chain Saws < 6 HP (Residential) Other Lawn and Garden Equipment (Residential) Leafblowers/Vacuums (Residential) Rotary Tillers < 6 HP (Residential) 176, ,626 86,746 82,010 78,543 71,733 Snowblowers (Residential) 51,147 Snowblowers (Residential) 26,785 Leafblowers/Vacuums (Residential) Rotary Tillers < 6 HP (Residential) Trimmers/Edgers/Brush Cutters (Residential) 9,136 6,497 4,792 Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 132

140 Table C-6: State-Level VMT Allocation Factors by Vehicle Type Among Commercial, Public, and Household/Private Uses Motorcycles LDGV 1 LDDV 2 LDGT 3 LDDT 4 HDGV 5 HDDV 6 State Com Priv Pub Com Priv Pub Com Priv Pub Com Priv Pub Com Priv Pub Com Priv Pub Com Priv Pub AL AK AZ AR CA CO CT DE DC FL GA HI ID IL IN IA KS KY LA ME MD MA MI MN MS MO MT NE NV NH NJ NM NY NC ND OH Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 133

141 Table C-6: State-Level VMT Allocation Factors by Vehicle Type Among Commercial, Public, and Household/Private Uses Motorcycles LDGV 1 LDDV 2 LDGT 3 LDDT 4 HDGV 5 HDDV 6 State Com Priv Pub Com Priv Pub Com Priv Pub Com Priv Pub Com Priv Pub Com Priv Pub Com Priv Pub OK OR PA RI SC SD TN TX UT VT VA WA WV WI WY Notes: 1 Light Duty Gasoline Vehicles 2 Light Duty Diesel Vehicles 3 Light Duty Gasoline Trucks 4 Light Duty Diesel Trucks 5 Heavy Duty Gasoline Vehicles 6 Heavy Duty Diesel Vehicles Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 134

142 C.3 Energy Use Table C-7: MECS Data Adjustments to 2002 Energy Consumption Data NAICS Code NAICS Description Data Description 3111 Animal food mfg Data not available Grain & oilseed milling Fuel Use: Data was available for NAICS Data withheld because the estimate is less than 0.5 for residual fuel oil, distillate fuel oil, and LPG/NGL. Remainder from total reported (1 unit) was allocated among the 3 fuel types withheld based on the proportion of those fuel types within NAICS 311. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within the total, distillate fuel oil and other fuels, these values were listed as zero. Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within LPG/NGL, this value was listed 3113 Sugar & confectionery product as zero. mfg Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within the total, distillate fuel oil, natural gas, coke and breeze, and other fuels, these values were listed as zero Fruit & vegetable preserving & specialty food mfg 3115 Dairy product mfg Data not available Animal slaughtering & processing Data not available Seafood product preparation & packaging Data not available Bakeries & tortilla mfg Data not available Other food mfg Data not available Beverages 3122 Tobacco 3131 Fiber, yarn, & thread mills Data not available Fabric mills Data not available Textile & fabric finishing & fabric coating mills Data not available Textile furnishings mills Data not available Other textile product mills Data not available Apparel knitting mills Data not available Cut & sew apparel mfg Data not available Apparel accessories & other apparel mfg Data not available Leather & hide tanning & finishing Data not available Footwear mfg Data not available Other leather & allied product mfg Data not available Sawmills & wood preservation Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within LPG/NGL, this value was listed as 1, due to the remaining 1 unit that was not reported among the fuel types. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within the total, residual fuel oil, distillate fuel oil and other fuels, these values were listed as zero. Fuel Use: Data reported at the 4-digit NAICS level. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within the total, residual fuel oil, distillate fuel oil and other fuels, these values were listed as zero. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within distillate fuel oil, LPG/NGL, and other fuels, these values were listed as zero. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within natural gas, these values were listed as zero. Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within residual fuel oil, this value was listed as zero. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within residual fuel oil and other fuels, these values were listed as zero. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 135

143 Table C-7: MECS Data Adjustments to 2002 Energy Consumption Data NAICS NAICS Description Data Description Code 3212 Veneer, Plywood, and Engineered Woods 3219 Other Wood Products 3221 Pulp, paper, & paperboard mills 3222 Converted paper product mfg Data not available Printing and Related Support 3241 Petroleum and Coal Products 3251 Basic chemical mfg Resin, syn rubber, & artificial syn fibers & filaments mfg Pesticide, fertilizer, & other agricultural chemical mfg 3254 Pharmaceuticals and Medicines Fuel Use: Data reported at the 4-digit NAICS level. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil, distillate fuel oil, and other fuels, these values were listed as zero. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil, this value was listed as zero. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within the total, distillate fuel oil and other fuels, these values were listed as zero. Fuel Use: Data was available for NAICS , , , and Due to data withheld within certain fuel types, the remaining units that were not reported among the fuel types were allocated among fuel types based on the proportion of those fuel types within NAICS 322. Due to data withheld because the estimate is less than 0.5, these values were listed as zero. Non-Fuel Use: Data was available for NAICS , , , and Due to data withheld because the estimate is less than 0.5 within certain fuel types, these values were listed as zero. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil and distillate fuel oil, these values were listed as zero. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within the total, distillate fuel oil and other fuels, these values were listed as zero. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within coke and breeze, this value was listed as zero. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil and natural gas, these values were listed as zero. Due to data withheld because the relative standard error is greater than 50 percent within coal, this value was as the remainder of the total. Fuel Use: Data was available for NAICS , , , , , , , and Due to data withheld within certain fuel types, the remaining units that were not reported among the fuel types were allocated among fuel types based on the proportion of those fuel types within NAICS 325. Due to data withheld because the estimate is less than 0.5, these values were listed as zero. Non-Fuel Use: Data was available for NAICS , , , , , , and (data was withheld for NAICS ). Due to data withheld within certain fuel types, the remaining units that were not reported among the fuel types were allocated among fuel types based on the proportion of those fuel types within NAICS 325. Due to data withheld because the estimate is less than 0.5, these values were listed as zero. Fuel Use: Data was available for NAICS , , and Due to data withheld within certain fuel types, the remaining units that were not reported among the fuel types were allocated among fuel types based on the proportion of those fuel types within NAICS 325. Due to data withheld because the estimate is less than 0.5, these values were listed as zero. Non-Fuel Use: Data was available for NAICS , , and Due to data withheld because the estimate is less than 0.5 within certain fuel types, the remaining units that were not reported among the fuel types were allocated among fuel types based on the proportion of those fuel types within NAICS 325. Data was available for NAICS , and Due to data withheld because the estimate is less than 0.5 within certain fuel types, these values were listed as zero. Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within LPG/NGL, this value was listed as zero. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 136

144 Table C-7: MECS Data Adjustments to 2002 Energy Consumption Data NAICS Code NAICS Description Data Description 3255 Paint, coating, & adhesive mfg Data not available Soap, cleaning compound, & toilet preparation mfg Data not available Other chemical product & preparation mfg 3261 Plastics product mfg Data not available Rubber product mfg Data not available Clay product & refractory mfg Data not available Glass and Glass Products 3273 Glass and Glass Products 3274 Lime & gypsum product mfg Other nonmetallic mineral product mfg Iron & steel mills & ferroalloy mfg Steel Products from Purchased Steel 3313 Alumina and Aluminum Foundries Nonferrous Metals, except Aluminum Fuel Use: Data was available for NAICS Due to data withheld within net electricity and coal, the remaining units that were not reported among the fuel types were allocated among these 2 fuel types based on the proportion of those fuel types within NAICS 325. Due to data withheld because the estimate is less than 0.5, these values were listed as zero. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within the total, net electricity, LPG/NGL, and other fuel, these values were listed as zero. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil and distillate fuel oil, the remaining 1 unit that was not reported among the fuel types was allocated among the 2 fuel types based on the proportion of those fuel types within NAICS 327. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within distillate fuel oil, natural gas, and coal, these values were listed as zero. Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within LPG/NGL, this value was listed as zero. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within the total, distillate fuel oil and other fuel, these values were listed as zero. Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within LPG/NGL and coke and breeze, these values were listed as zero. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within the total, distillate fuel oil, coal, and other fuel, these values were listed as zero. Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within LPG/NGL and other fuel, these values were listed as zero. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within distillate fuel oil and other fuel, these values were listed as zero. Data reported at the 4-digit NAICS level. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil, distillate fuel oil, and LPG/NGL, these values were listed as zero. Non-Fuel Use: Data not available; withheld because the relative standard error is greater than 50 percent. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil and coke and breeze, these values were listed as zero. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within distillate fuel oil and coal, these values were listed as zero. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil, this value was listed as zero. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil and distillate fuel oil, these values were listed as zero. Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil and other fuels, Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 137

145 Table C-7: MECS Data Adjustments to 2002 Energy Consumption Data NAICS Code NAICS Description Data Description these values were listed as zero. Non-Fuel Use: Data reported at the 4-digit NAICS level. Due to data withheld because the estimate is less than 0.5 within residual fuel oil, distillate fuel oil and LPG/NGL, these values were listed as zero Forging & stamping Data not available Cutlery & handtool mfg Data not available Architectural & structural metals mfg Data not available Boiler, tank, & shipping container mfg Data not available Hardware mfg Data not available Spring & wire product mfg Data not available Machine shops, turned product, & screw, nut, & bolt mfg Data not available Coating, engraving, heat treating, & allied activities Data not available Other fabricated metal product mfg Data not available Agriculture, construction, & mining machinery mfg Data not available Industrial machinery mfg Data not available Commercial & service industry machinery mfg Data not available Ventilation, heating, AC, & commercial refrigeration equip Data not available. mfg 3335 Metalworking machinery mfg Data not available Engine, turbine, & power transmission equipment mfg Data not available Other general purpose machinery mfg Data not available Computer & peripheral equipment mfg Data not available Communications equipment mfg Data not available Audio & video equipment mfg Data not available Semiconductor & other electronic component mfg Navigational, measuring, medical, & control instruments mfg Mfg & reproducing magnetic & Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within residual fuel oil, distillate fuel oil, and LPG/NGL, the remaining 1 unit that was not reported among the fuel types was allocated among the 3 fuel types based on the proportion of those fuel types within NAICS 334. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within natural gas, this value was listed as zero. Data not available. Data not available. optical media 3351 Electric lighting equipment mfg Data not available Household appliance mfg Data not available Electrical equipment mfg Data not available Other electrical equipment & component mfg 3361 Motor vehicle mfg Data not available. Fuel Use: Data was available for NAICS Due to data withheld within net electricity and coal, the remaining units that were not reported among the fuel types were allocated among the 2 fuel types based on the proportion of those fuel types within NAICS 336. Due to data withheld because the estimate is less than 0.5 within residual fuel oil, distillate fuel oil, and LPG/NGL, these values were listed as zero. Non-Fuel Use: Data was available for NAICS Due to data withheld because the estimate is less than 0.5 within distillate fuel oil and other fuel, Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 138

146 Table C-7: MECS Data Adjustments to 2002 Energy Consumption Data NAICS Code NAICS Description Data Description the remaining 1 unit that was not reported among the fuel types was allocated among the 2 fuel types based on the proportion of those fuel types within NAICS Motor vehicle body & trailer mfg Data not available Motor vehicle parts mfg Data not available Aerospace product & parts mfg Data not available Railroad rolling stock mfg Data not available Ship & boat building Data not available Other transportation equipment mfg Data not available Household & institutional furniture & kitchen cabinet mfg Data not available Office furniture (including fixtures) mfg Data not available Other furniture related product mfg Data not available Medical equipment & supplies mfg Data not available Other miscellaneous mfg Data not available. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 139

147 Table C-8: Adjustments to 2002 Economic Census Mining Energy Consumption Data 4-digit/6--digit NAICS Coal Distillate Fuel Oil Gas Gasoline Residual Fuel Oil 2111: Oil and Gas Extraction : Coal Mining : Metal Ore Mining Data not available. Data only available for NAICS and Data only available for NAICS Data only available for NAICS Data withheld for NAICS ; estimated based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data only available for NAICS (which is 10 to 19 percent estimated). Data estimated for NAICS and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data only available for NAICS and (20 to 29 percent estimated for NAICS ). Data estimated for NAICS and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data only available for NAICS and (Census notes that value is 10 to 19 percent estimated). Data estimated for NAICS based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data not available for the remaining NAICS codes. Data only available for NAICS and (Census notes that values are 10 to 19 and 20 to 29 percent estimated, respectively). Data estimated for NAICS based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data not available for the remaining NAICS codes. Data only available for NAICS and (which is 10 to 19 and 20 to 29 percent estimated, respectively). Data only available for NAICS (which is 10 to 19 percent estimated). Data estimated for NAICS based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data estimated for NAICS based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data not available for the remaining NAICS codes. Data only available for NAICS and (Census notes that value is 20 to 29 percent estimated). Data only available for NAICS Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 140

148 Table C-8: Adjustments to 2002 Economic Census Mining Energy Consumption Data 4-digit/6--digit NAICS Coal Distillate Fuel Oil Gas Gasoline Residual Fuel Oil 2123: Nonmetallic Mineral Mining and Quarrying : Support Activities for Mining Data only available for NAICS Data estimated for NAICS based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data not available. Data only available for NAICS , , , , , and (20 to 29 percent estimated for NAICS , , , and ; 10 to 19 percent estimated for NAICS and ). Data estimated for NAICS , , , , and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data only available for NAICS (which is 20 to 29 percent estimated). Data estimated for NAICS , , , and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data only available for NAICS , , and (data for NAICS and is 10 to 19 percent estimated; data for NAICS is 20 to 29 percent estimated). Data estimated for NAICS , , , , , and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data only available for NAICS (which is 10 to 19 percent estimated). Data estimated for NAICS and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data only available for NAICS , , and (data for NAICS is 10 to 19 percent estimated; data for NAICS is 20 to 29 percent estimated). Data estimated for NAICS , , , , and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data estimated for NAICS , , , and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data not available for the remaining NAICS code. Data only available for NAICS and (which is 20 to 29 percent estimated). Data estimated for NAICS , , , , and based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Data only available for NAICS (which is 10 to 19 percent estimated). Data estimated for NAICS based on the delivered quantity ($) and the average delivered quantity ($)/consumption quantity ratio for this fuel type. Abt Associates, September 29, 2009 Trade and Environmental Assessment Model: Model Description 141