# EPA Optimization Model for Reducing Emissions of Greenhouse Gases from Automobiles (OMEGA)

Model Documentation 1.0EPA-420-B-09-035 October 2009 Assessment and Standards Division Office of Transportation and Air Quality U.S. Environmental Protection Agency 

# EPA Optimization Model for Reducing

Emissions of Greenhouse Gases from Automobiles (OMEGA)

Model Documentation 1.0I. Model Overview

On-road vehicles are the predominant s ource of greenhouse gas (GHG) em issions from the transportation sector. Of all on-road vehi cles, light-duty vehicles and light-duty trucks (hereafter referred to as cars and trucks) produce the majority of the GHG emissions. There are many methods for reducing GHG emissions from cars and trucks due to the myriad of technology options available to impr ove the efficiency of vehicles . A detailed analysis of the costs and benefits of various GHG emissions re duction requires a specialized application that optimizes and accounts for all the promisi ng technologies, going beyond what can be accomplished with simple spreadsheet tools. Th erefore, EPA’s Office of Transportation and Air Quality (OTAQ) has developed the Optimizati on Model for reducing Emissions of Greenhouse gases from Automobiles (hereafter referred to as the “OMEGA” m odel) to help facilitate the analysis of the costs and benefits of re ducing GHG emissions from cars and trucks..

Broadly speaking, the OMEGA model evaluates the relative cost a nd effectiveness of available technologies and applies them to a defined vehicle fleet in order to meet a specified GHG emission target. Once the target has been me t, OMEGA reports out th e cost and societal benefits of doing so. Presently, OMEGA m odels two types of GHGs, carbon dioxide (CO2) from fuel use and refrigerant emissions from th e air conditioning (A/C) sy stem. The core model utilizes the J# programming language. However, both inputs to and outputs from the model are provided using spreadsheet files, providing accessibility to a wide range of users. The spreadsheet output files also faci litate additional manipulation of th e results desired by the user.

OMEGA is primarily an accounting model. It is not a vehicle si mulation model, where basic information about a vehicl e, such as its mass, aerodynamic drag, an engine map, etc. are used to predict fuel consumption or CO2 em issions over a defined driving cycle. While OMEGA incorporates functions which generally minimize the cost of meeting a specified CO2 target, it is not an economic simulation model whic h adjusts vehicle sales in response to the cost of the technology added to each vehicle. While vehicle sales can change over time, vehicle sales do not change in response to the addition of technology. OMEGA may be expanded in the future to incorporate such market responses. Howeve r, currently, such responses must be addressed “outside of the model” through sequential model runs with market adjustments made between

runs.

OMEGA can be used to model either a singl e vehicle model or over a thousand vehicle models). Vehicles can be those of specific ma nufacturers or generic fl eet-average vehicles. Because OMEGA is an accounting model, the ve hicles comprising the vehicle fleet being evaluated can be described using only a relatively few number of terms. The most important of these terms are the vehicle’s baseline emissi on level, the level of CO2 reducing technology already present, and the vehicle’s “type,” which indicates th e technology availabl e for addition to that vehicle. Information required to determ ine the applicable CO2 emission target for the vehicle must also be provided. This may simply be vehicle class (car or truck) or it may also include other vehicle attri butes, such as footprint. 1

1 A vehicle’s footprint is the product of its track width and wheelbase, usually specified in terms of square feet.

1GHG emission control technology can be applie d individually or in groups, often called technology “packages.” The user sp ecifies the cost and effectiveness of each technology or package for a specific “vehicle type,” such as mi dsize cars with V6 engines or minivans. The user can limit the applica tion of a specific technology to a spec ified percentage of each vehicle’s sales. The effectiveness and application limits of each techno logy package can also vary over time. Technology costs are currently held constant over time. Futu re versions of the model may incorporate either the ab ility to vary cost over time or inco rporate “learning” into the technology cost estimation procedure. Learning is the pro cess whereby the cost of manufacturing a certain item tends to decrease over time due to experi ence. A list of technol ogies or packages is provided for each vehicle type, providing the conn ection to the specific vehicles being modeled.

OMEGA is designed to apply technology in a manner similar to the way that a vehicle manufacturer might make such decisions. In ge neral, the model considers three factors which EPA believes are important to th e manufacturer: 1) the cost of the technology at the consumer level, 2) the value which the consumer is li kely to place on improved fuel economy and 3) the degree to which the technology moves the manufact urer towards its CO2 emission target.

Technology can be added to individual vehicles using three distinct ranking approaches at present. (Additional ranking approaches are pla nned in the future in order to provide greater flexibility to the user.) Within a vehicle type, the order of tec hnology packages is set by the user. The model applies technology to the vehicle with the lowest Technology Application Ranking Factor (hereafter referred to as the TARF). One TARF considers only th e cost of the technology and the value of any reduced fuel consumption c onsidered by the vehicle purchaser. The other two also consider the mass of GHG emissions redu ced over the life of the ve hicle. Fuel costs by calendar year and annual vehicles travelled per vehi cle are provided by the user to facilitate these calculations.

For each manufacturer, OMEGA applies tec hnology to its vehicles until its salesweighted GHG emission average complies with the specified GHG emission standard or until all the available technologies have been applied. Th e GHG emission standard can be a flat standard applicable to all vehicles with in a vehicle class (e.g., cars, truc ks or both cars and trucks). Alternatively the GHG standard can also be in the form of a linear or constrained logistic function, which sets each vehicle’s target as a function of vehicle footprint (vehicle track width times wheelbase). When the linear form of foot print-based standard is used, the “line” can be converted to a flat standard for f ootprints either above or below sp ecified levels. This is referred to as a segmented line ar standard.

The GHG emission target can vary over time, bu t not on an individual model year basis. One of the fundamental features of the OMEGA model is that it applies technology to a manufacturer’s fleet over a sp ecified vehicle redesign cycl e. OMEGA assumes that a manufacturer has the capability to redesign any or all of its vehicles within this redesign cycle. OMEGA does not attempt to determine exact ly which vehicle will be redesigned by each manufacturer in any given model year. In stead, it focuses on a GHG emission goal several model years in the future, reflecting the capability of mid to long term planning on the part of manufacturers. Any need to fu rther restrict the application of technology can be effected through the caps on the application of technology to ea ch vehicle type mentioned above. When

2calculating annual costs and benef its of a particular GHG standard or standards, OMEGA uses linear interpolation to de termine standards in th e interim model years. Annual standards which do not vary linearly can be m odeled by the user, but must be manually input to the spreadsheet which calculates costs and benefits.

GHG emission standards are specified in terms of CO2 equivalent emissions. These CO2 equivalent emissions can be based on any test pr ocedure. The only requirement is that the base CO2 emissions specified for each vehicle and the effectiveness of the specified technologies be based on the same test cycle. For example, these emissions can simply be those from the tailpipe as measured over the current two-cycle CAFE test procedure or they can also include tailpipe CO2 emissions from air conditioner use, as well as refrigerant emissions from the air conditioning system. In the case of the latter , the descriptions of vehicle emissions and technologies must include baseline refriger ant emissions and the effectiveness of each technology in reducing these emi ssions. GHG emissions could al so be based on the 5-cycle formulae soon to be used to calculate a vehicle’s fuel economy label. The user simply needs to take care to specify CO2 and refrigerant emissi on and technology effec tiveness estimates which are based on emissions over all five emission test s and as combined according to the five-cycle formulae. OMEGA bases all of its calculations on combined test cycle emissions. Compliance cannot be specified for two distinct test cycles, for example, city and highway test emissions. Only combined city-highway emissions can be modeled.

Once technology has been added so that ev ery manufacturer meets the specified targets (or exhausts all of the available technologies), th e model produces a wide va riety of output files. Outputs include specific information about the technology added to each vehicle and the resulting costs and emissions. Average costs pe r vehicle by manufacturer and industry-wide are also determined for each vehicle class.

Fleet-wide average GHG emissions are also de termined for each redesign cycle. As mentioned above, emissions and costs are estimat ed for each calendar year in between redesign cycles and are used to estimate a wide array of societal costs and benefits associated with the GHG emission control. The primary cost of GH G emission control is th e cost of the added technology as compared to the baseline or referen ce case. The primary benefit is the value of reduced fuel consumption and is a function of the price of fuel input to the model. In addition, the value of a number of other costs and benefits are also qu antified, as listed below:

1) The reduction in carbon monoxide (CO), vol atile organic compounds (VOC), nitrogen oxides (NOx), particulate matter (PM), and sulfur oxides (SOx) emissions associated with reduced fuel production, as well as their monetized value, 2) Externalities associated with reduced crude oi l use which are not reflected in the price of crude oil, such as price shock impacts, monopolistic 3) The value of reduced time necessary to refuel vehicles, and 4) The value of GHG emission reductions.

GHG emission control usually improves vehicl e fuel economy and thus, reduces the cost of driving. Studies have shown that vehicles are often driven more if their fuel economy increases. This is commonly referred to as th e rebound effect. This effect, usually specified as

3the percentage increase in VMT per percentage d ecrease in fuel consumption per mile, can be specified by the user. OMEGA output spreadsheets estimate of a number of potential costs and benefits associated with driving, as follows:

1) Increased vehicular CO, VOC, NOx, SOx, and PM emissions and their monetized value, 2) Increased vehicular noise, congestion, and accidents, 3) The value of the additional driving, and 4) The cost of fuel required by the additional driving.

This version of the OMEGA documentation is being published with the input files used to support EPA’s proposed GHG emission control pr ogram for cars and light trucks. EPA has established a webpage for the OMEGA model on the EPA agency website (go to www.epa.gov/otaq /climate/models.htm). This webpage will provide additional information about OMEGA. Periodic updates of both the model and this documentation will also be available to be downloaded. Those interested in using the model for purposes other than reviewing its use in support of the above mentioned NPRM are encouraged to periodically check this website for these updates.

The remainder of this document is divided in to four sections. The first section describes the information which can or must be input to the model. The second section describes the application of technology in order to comply with the specified sta ndards, what we define as the “core model.” The third section describes the various output files produced by the model. The fourth section describes the steps necessa ry to run or operate the OMEGA model.

II. Input Files

OMEGA is designed to be flex ible in a number of ways. Ve ry few numerical values are hard-coded in the model, and consequently, the m odel relies heavily on its input files. The core model utilizes five input files: Market, Tec hnology, Fuels, Scenario, and Reference. Many values are utilized to estimate societal cost s and benefits and are included in the benefit calculation spreadsheet, which is primarily consider ed to be an output of the model. Thus, the user’s flexibility to vary thes e inputs will be discussed when th e benefit calcula tion spreadsheet is discussed in Section IV below.

All the input files ar e Microsoft® Excel® sp readsheets. The headings of the various types of input data are contained in Row 1 of each worksheet. It should be noted that these headings cannot be modified by the user. Each input file also contains two common types of worksheets. One is named “Validation List” and lists the types of value allowed in each column along with an allowed range of valid values. This worksheet is typically hidden and can be viewed using the Format/Sheet/Unhide command. Th e user can change the range of valid values as desired. The user should note change the type of data shown as the core model is designed to look for certain types of information in each colu mn and changing the input file does not affect this expectation.

The second common worksheet is named “Erro rs.” This worksheet contains a button labeled “Validate Data” which can be selected. When selected or “pushed,” the spreadsheet runs

4through the various input worksheets and determines if all values f it the expected type of data and fall within the allowed range of values. Cells that do not fit the criteria are listed and the problem described. This function can be very useful when developing new input files by reducing the number of aborted model runs.

It is important to note that OMEGA expects all the cells to the right of the last column of required data to be blank and, more specifically, to have never been written into. If these cells are used for temporary calculations, the user should go further to the right to a column which has never been written into and copy the entire colu mn of blank cells into the columns which have been used temporarily. The e rror identificati on process will then re cognize these cells as actually being blank. The same is true for all rows beneath the last row of actual data.

A. The Market File: Vehicle Fleet Characterization

The market input file contains essentially all the required information which describes the vehicles being modeled. This file consists of three worksheets in addition to the Validation List and Errors worksheets: Market Data, Vehicle Type , and Refrigerant. Each will be discussed in turn below.

Table II-1 shows the various types of data included in the market data worksheet.

Table II-1 Input Data in Market Da ta Worksheet of the Market File Column Name Column Name

A Vehicle Index No U Horsepower

B Manufacturer V Max Seating

C Model W Transmission Type

D Vehicle Type No X Drive

E Vehicle Class Y Structure

F Baseline Sales Z Internal Volume

G through N Annual Sales - Cycles 1 - 8 AA Primary Fuel Type

O Combined FE (mpg) AB Combined EC (kWh/mi)

P Tailpipe CO2 (g/mi) AC Refrigerant Type

Q Footprint (ft2) AD Refrigerant Lifetime Leakage (g)

R Curb Weight (lb) * AE through AX TEB Tech. Pkg. 1 - 20

S No. of Cylinders AY Through BR CEB Tech. Pkg. 1 - 20

T Displacement (L) * Columns highlighted in gray are not currently used by OMEGA

5Column A contains a unique , positive nume rical identifier for each vehicle being modeled. The indices do not need to be in numer ical order, just unique and positive. Column B contains the name of each manufacturer. There ar e no requirements with respect to the names, except that the user needs to take care that vehicles intended to be produced by the same manufacturer are given exactly the same name. Slight differences in spelling or spacing will cause the model to treat them as separate manuf acturers. Column C contains an alpha-numeric name for each vehicle. These names do not need to be unique, simply to be as descriptive as the user desires.

Column D contains the vehicle type code . OMEGA currently allows up to 20 vehicle types, numbered with integers 1-20. This code provides the connection between the vehicles listed in the market data worksh eet and the available technologies listed in the Technology file.

Column E contains the vehi cle class designator. Current ly OMEGA can model up to two vehicle classes: cars indicated with “C” and trucks indicated with “T.” This code provides the connection between the vehicles li sted in the market data worksh eet and the standards listed in the Scenario file. If only one standard applic able to both cars and tr ucks is being modeled, vehicles can be labeled as eith er cars or trucks or both. If both vehicle labels are used, compliance will still be based on the combination of both vehicle classes, but emissions will be tracked separately in the model’s outputs. This latter situation is advantageous if cars and trucks are to have distinct scrappage rate s and annual travel estimates.

Column F contains baseline sales. The baseline is the model year prio r to the first year of the first redesign cycle being modeled. Baseline sales are not used in the core model to add technology to facilitate compliance; instead they are used in orde r to linearly in terpolate annual vehicle sales during the interim years between the baseline year and the y ear of the end of the first redesign cycle. Sales must simply be positive and may even be fractional.

Columns G through N contain sales for the la st year of each of up to eight redesign cycles. Sales must be positive and may even be fractional. Currently, the model assumes that a redesign cycle is five years long. Future versions may allow the user to specify the length of each redesign cycle. Sales must be entered for eac h vehicle, even if the value of sales is zero. Values must only be entered for redesign cycles actually being modeled. Thus, if only one redesign cycle is being modeled, then sales must only be entered in Column G. Columns H through N can be blank in this cas e. Sales can change in any manner between redesign cycles. Costs and benefits for interim years will be based on linearly interpolated sales per year.

Column O contains each vehicle’s baselin e fuel economy value in miles per gallon (mpg). It is currently not used by the model. EPA intends to develop a future version of OMEGA which will evaluate compliance in terms of fuel economy in addition to GHG emissions.

Column P contains each vehicle’s baseline CO2 emissions in grams per mile (g/mi) over whichever test cycle or cycles comprise the basi s for compliance with the standards described in the Scenario file. This value should not in clude the CO2 equivalent emissions related to refrigerant leakage emissions. Those emissions are described in later columns. The CO2

6emissions of Column P could include the CO2 equivalent em issions of GHGs like methane and nitrous oxide, as long as the e ffectiveness of the technologies described in the Technology file and the standards listed in the Scenario file c onsidered these emissions in a consistent manner.

Column Q contains each vehicle’ s footprint value in square feet. This value is used to determine each vehicle’s CO2 emission standard when this standard is either a constrained logistic curve or linear function.

Columns R through Z describe several vehicle attributes which are not currently used by the model. These are included to facilitate future versions of the model which may base compliance or evaluate model output using one or more of these attributes.

Column AA contains a designator for each ve hicle’s primary fuel type. Three types are currently allowed in the model: G for gasoline, D for diesel fuel and E for electric or battery electric vehicle. Plug in hybr id vehicles would be designate d by either G or D, depending on which liquid fuel they used. The model will c onsider the electricity us ed by plug in vehicles based on the value of electricity consumption per mile described in Column AB, which is described below. The model does not at present allow for other fuels, such as E85, compressed or liquefied natural gas, or liq uid petroleum gas. Future vers ion of OMEGA may include these fuel types.

These vehicles can usually be included in the current model by simply designating their fuel as either G or D depending on the type of engine being utilized by the alternative fueled vehicle. For example, a dedicated E85 vehicle could be designated as a gasoline-fueled vehicle since the type of technologies app licable to both vehicl es are very similar if not identical. The base emissions listed in Column P would simp ly be those emitted by the E85 vehicle when operating on E85. The addition of technology to facilitate compliance should be correct. The error would occur in the benefit calculation file, where the fuel savings calculated would be in terms of gasoline and not E85. The user would ha ve to determine the percentages of pre- and post-control fuel consumption which are in terms of gasoline and E85 and apply these percentages to the model’s esti mates of gasoline consumption

The fuel economy of flex-fuel vehicles (FFV s) is currently determined on the basis of their fuel economy while operated on gasoline, with an adjustme nt factor to incentivize the production of FFVs. EPA is proposing a sim ilar approach in its proposed GHG emission regulations for cars and light trucks . If the FFVs are already in th e baseline fleet or the user does not desire to include the cost of adding flex fuel technology, this FFV factor could be included in determining the base CO2 emissions of the FFV, as discussed above for dedicated alternative fueled vehicles. Depending on whether the user e xpects such vehicles to actually be operated on gasoline or E85, the fuel consumption figures in the benefits calculation file may or may not require adjustment.

However, there are usually limits on the ove rall credit which a manuf acturer can receive from such FFV incentives. Including the FFV incentive in the baseline CO2 emission values could exceed such limits. If a manufacturer is expected to utilize the maximum allowed FFV credit, it will likely be easier to adjust the manufacturer’s st andard for this credit when

7determining which standard to include in the S cenario file. EPA will soon publish a version of OMEGA which will allow the user to specify credits such as an FFV credit either in terms of fuel economy (mpg) or CO2 emissions (g/mi). This cr edit will be described in more detail below when we describe the input to the Scenario file in Section II.D. This will facilitate the modeling of multiple manufacturers’ vehicles with FFV or other credits which vary by manufacturer and over time.

If the user desires to model the conversi on of gasoline-fueled vehicles to FFVs, a technology which basically performs this conversion could be included in the Technology file. Its effectiveness would reflect the FFV credit granted under the particular GHG control program being modeled, plus any actual change in CO 2 emissions when the vehicle is operated on gasoline. This approach requires users’ carefu l consideration to place the FFV technology step in the appropriate place in the order of technolog ies for each applicable vehicle type. The fuel savings implied by the FFV credit is usually very large and the cost of the conversion technology is usually relatively small. Thus, this technology will appear to be very cost effective from the model’s perspective regardless of which TARF is selected. Thus, unde r the typical CAFE FFV credit scenario, this technology shou ld be placed early in the list of technologies, as discussed in more detail in the next section, if not first.

With this approach, it may be possible to re strict manufacturers’ use of FFV credits to reflect real-world regulatory limits on the overall use of such credits. The overall use of the FFV technology can be limited in the Technology file, as discussed below. Its use on individual vehicles can also be limited (e.g., to reflect different manufacturers’ approaches to FFV credits) by showing certain vehicles to already have such technology even if they do not. As discussed further below, if a vehicle already has a technology, the model will go through the step of adding the technology, but will not add th e cost of this technology nor ch ange the vehicle’s emissions. It might take a number of model runs to get the effective level of the FFV credit for each manufacturer close to the regulatory limit.

Column AB contains the vehicle’s baseline el ectricity consumption in units of kilowatthour per mile (kw-hr/mi). At present, this va lue is only used in calculating the Technology Application Ranking Factor (TARF) , in which a comparison must be made between vehicles and technologies which may consume different types of energy sources, such as liquid gasoline or diesel, or electricity. Since consumers tend to make decisions on which vehicle to purchase based on how much the vehicle costs them to dr ive it, this column helps the model account for the any energy in the baseline vehicles which is supplied by the grid. This term only applies to plug in hybrids and battery electric vehicles.

At present, the OMEGA model assumes that compliance with the input CO2 emission standard is based on vehicle emissions alone. Si nce the use of electricity produces no emissions from the vehicle, the level of electricity usage per mile is not used in the model’s compliance calculation. Future versions of the model may a llow the user to assign a CO2 emission rate to electricity use to represent CO2 emitted in the pr oduction of electricity. The benefits calculation file also does not track electricity use. Future versions of OMEGA will allow the user to input a value for the GHG emissions associated with gene rating a kw-hr of electricity when determining compliance with the GHG standards. It is possible th at in a future version, users will be able to

8input different levels of GHG emi ssions varying with time into the benefits calculation file to estimate ann ual emission impacts associated with changing electricity use.

Column AC designates the type of refrigeran t used by the vehicle. This designator must match one of the types of refrigerant listed on the Refrigerant worksheet of the Market file described further below. The primary purpose of this designation is to specify the global warming potential (GWP) of the refrigerant so that mass emissions of refrigerant leakage can be converted to their CO2 equivalent emissions. In version 1.0 of OMEGA, the GWPs are hardcoded, thus requiring the user to sp ecify one of the refrigerants in the list on the refrigerants page of the market file. In the next release of OM EGA, EPA is planning to allow editable refrigerant data in the References file, such that the user can edit or add additional refrigerant names and GWPs.

Column AD of the market file designates the lifetime leakage of the refrigerant, which the model uses to calculate the leakage rate in CO2 equivalents. EPA c hose to input the lifetime leakage instead of the leakage ra te because the lifetime leakage is easier to quantify. The relative distribution of leakage emissions across the life the vehicle is ha rd-coded within the model, and OMEGA uses this to convert refrigerant leakage to grams of CO2 equivalents per mile. This process is described in section III.B below.

Columns AE through BR are used to track t echnology that may be pr esent in the baseline or reference case. This data is necessary to prevent the model from double counting technology costs and GHG improvement. Columns AE thr ough AX represent the fraction of the technology package effectiveness for the different vehicles that is present in the reference case; for example, a value of 35% for technology package 1 means that 35% of the eff ectiveness of technology package 1 on that vehicle type is already pres ent in the reference case. Columns AY through BQ represent the fraction of the tec hnology packages’ cost that is reflected in the reference case. Likewise, a value of 75% in any of these column s means that 75% of the technology package’s cost on the particular vehicle has been included in the reference case.

The first step in determining these fractions, refe rred to as the TEB and CEB in the market input file, is to develop a list of i ndividual technologies which are e ither contained in each technology package, or would supplant the relevant portion of each technology package (e.g., the engine, the transmission, etc.). For example, variable intake valve timing would be associated with a downsized, turbocharged, direct injection engine . Thus, the effectiveness and cost of variable intake valve timing would be considered to be alrea dy present for any technology packag e which included either variable intake valve timing or included an engine technology which provided grea ter effectiveness. The reverse case would be an example of a technology which would supplant another technology. If a vehicle already had a downsized, tu rbocharged, direct injec tion engine, the effectiveness and cost of this technology would be considered to be already fully present when evaluating the application of a technology package which included va riable intake valve timing or any other engine technology up to and including a downsized, turbocharg ed, direct injection engine. In either case, the effectiveness and cost present on the 2008 MY vehicle would be limited to the effectiv eness and cost of the engine technology. This would allow the app lication of non-engine related tech nologies also included in that package (e.g., an improved transmis sion) to still be applied.

9A specific example would be a 2008 MY vehicle fa lling into EPA vehicle type 1 that is equipped with a dry DC T. A dry DCT is available as pa rt of EPA’s technology package number 3 for vehicle type 1. Thus, when package 3 is added to that vehicl e, the TEB and CEB values in the market input file indicate to the model that this technology is already present, and the model will not add additional effectiveness and cost of a dry DCT when applying the effectiveness and cost of package 3 to this vehicle. If the dry DCT contributed to 50% of the total effectiveness and 40% of the cost of package 3 over package 2 and 40% of the cost of package 3 over package 2 for this vehicle, then these percentages would be included in the market data file for this vehicle. If the level of CO2 control led to the application of technology package 3 to this vehicle in a run of the OMEGA model, the model would only apply 50% of the effectiveness of package 3 to this vehicle and 60% of the cost of this package.

If two consecutive technology packages both contai n a dry DCT, packages 3 and 4, for example, the model user does not need to declare it as tech nology already present in th e baseline. Instead, the benefit of package 4 is due to e ngine-related technology is considered incremental to package 3 and is contained in the technology input file. When appl ying package 4 to this ve hicle, the OMEGA model will apply its full incremental effectiveness and cost . The same would be true for packages which replace a dry DCT with a hybrid technology equipped with variab le valve timing and a 6-speed automatic transmission. The cost and effectiveness of variable valve timing would be considered to be already present for any technology packages which in cluded the addition of va riable valve timing or technologies which went beyond this technology in terms of engine rela ted CO2 control efficiency. Since vehicle models are grouped in to vehicle types for technology add ition, and there are relatively few vehicle types compared to the numbe r of vehicle models analyzed, ther e may be special cases when the applicable technology packages are less effective th an baseline technologies; EPA therefore limits the applicability of the relevant tec hnology packages to the affected vehicle models by adjusting the cost and effectiveness reflected in the baseline by the same method describe d above. An example of a single technology which supplants several technologies would be a 2008 MY ve hicle which was equipped with a diesel engine. The effectiveness of this technol ogy would be considered to be present for technology packages which included small improvements to a gaso line engine, since the re sultant gasoline engine would otherwise accrue technology pack ages which are not as effective at reducing CO2 as the baseline diesel engine. However, if these packages wh ich included improvements al so included improvements unrelated to the engine, like tran smission improvements, only the engine related portion of the package already present on the vehicle would be considered. The transmission related portion of the package’s cost and effectiveness would be al lowed to be applied in order to comply with future CO2 emission standards.

The second step in this process is to determin e the total cost and CO 2 effectiveness of the technologies already present and rele vant to each available package. Determining the total cost is usually as simple as adding up the co sts of the individual technologies present. In order to determine the total effectiveness of the technolog ies already present on each vehicle, EPA uses its Lumped Parameter Model. Because the specific technologies pres ent on each 2008 vehicle are known, the applicable synergies and dis-synergies can be fully accounted for.

The third step in this pro cess is to divide the total cost and CO2 effectiveness values determined in step 2 by the total cost and CO2 effectiveness of the relevant technology packages. These fractions are capped at a value of 1.0 or less, since a value of 1.0 causes OMEGA to maintain both the cost and CO2 emissions of a vehicle when th at technology package is added.

10The fourth step is to combine the fractions of the cost and effectiv eness of each technology package already present on the individual 2008 ve hicl es models for each vehicle type. For cost, percentages of each package already present are comb ined using a simple sales-weighting procedure, since the cost of each package is the same for each vehicle in a vehicle type. For effectiveness, the individual percentages are combined by weighting them by both sales and base CO2 emission level. This appropriately weights vehicle models with either higher sales or CO2 emissions within a vehicle type. Once again, this process prevents the model from adding technology which is already present on vehicles, and thus ensures that the model does no t double count technology effectiveness and cost associated with complying with the 2011 MY CAFE standards and the propos ed CO2 standards.

Users interested in developing their own set of technologies and their re lated effectiveness and cost values must also develop new values of CEB and TEB for each vehicle. Section 4.2 of EPA’s Draft Regulatory Impact Analysis to the Agency’s propos al to establish GHG emissions standards for motor vehicles contains a detailed descri ption of the development of CEB and TEB values for a complex set of technologies coupled with a wide range of tec hnologies existing in th e baseline fleet.

B. The Technology file: Technology Package Characterization

Since there is large number of technologies which reduce CO2 emissions and a wide array of different vehicle systems to which they apply, the ma nufacturers’ design and production processes play a major role in determining the technology cost a ssociated with lowering fleet-wide CO2 emissions. Vehicle manufacturers typically develop several un ique models based on a limited number of shared vehicle platforms, allowing for efficient use of design and manufacturing res ources. The platform typically consists of common vehi cle architecture and structural co mponents. Given the very large investment put into designing and producing each ve hicle model, manufacturers typically plan on a major redesign for the models approximately every 5 year s. At the redesign stage, the manufacturer will upgrade or add all of the technology a nd make all of the other changes ne eded in order that the vehicle model will meet the manufacturer’s plans for the next several years. This includes meeting all of the emissions and other requirements that would apply during the years before the next major redesign of the vehicle.

This redesign often involves a package of changes, designed to work togeth er to meet the various requirements and plans for the model for several m odel years after the redesi gn. This often involves significant engineering, developmen t, manufacturing, and marketing re sources to create a new product with multiple new features. In order to leverage th is significant upfront investment, manufacturers plan vehicle redesigns with several model years’ of pr oduction in mind. Vehicle models are not completely static between redesigns as limited changes are often incorporated for each model year. This interim process is called a refresh of th e vehicle and generally does not allow for major technology changes although more minor ones can be done (e.g., aerodynami c improvements, valve timing improvements). More major technology upgrades that affect multiple sy stems of the vehicle thus occur at the vehicle redesign stage and not in the time period between redesigns.

There are a wide variety of emissions contro l technologies which involve several different vehicle systems. Many can involve major changes to the vehicle, such as cha nges to the engine block and heads, or redesign of the transmission and its p ackaging in the vehicle. This calls for tying the

11incorporation of the emissions control technology in to the periodic redesign pr ocess. This approach reflects manufacturer capability to develop appropriate packages of technology upgrades that combine technologies in ways that work together and fit with the overall goals of the rede sign. It also reflects the reality that m anufacturers fit th e process of upgrading emissions c ontrol technology into its multi-year planning process, and it avoids th e large increase in resources and co sts that would oc cur if technology had to be added outside of the redesign process.

The technology input file defines the technol ogy packages which the model can add to the vehicle fleet. For each vehicle type, the user must add a separate row for each technology package in the order of how OMEGA should add th em to that specific vehicle type. This approach puts considerable onus on the user to develop a reasonable sequence of technologies. However, the model also produces output inform ation which can help the user determine if a particular technology package might be “out of order”. The package approach also simplifies the model’s calculations and enable s synergistic effects among technology packages to be included to the fullest degree possible.

The “TechPacks” tab contains all of the data used by the model. Similar to the Market file, the Technology file contains an additional tab enti tled “errors”, which is used for validation purposes. Table II-2, below describes the data in the “TechPacks” tab.

Table II-2 Input Data in TechPack s Worksheet of the Technology File
Column(s) Contents

A Vehicle type number

B Technology package number

C Technology package description

D Abbreviation

E-L Market penetrati on caps by redesign cycle

M-T Incremental tailpipe CO2 emission control effectiveness

U Refrigerant emission control effectiveness

V Refrigerant type (NC = no ch ange from previous step)

W Primary fuel after applica tion of the technology package

X Electricity consumption after application of the technology package (if applicable)

Y Incremental technology cost

12Z-AD Placeholders for learn ing curve coeffici ents (not currently used by the model)

The data in this file can be categorized in four ways: 1) Information which describes the individual packages (name and vehicle type to wh ich it applies, for example; 2) Parameters the model uses to calculate CO2 improvement, such as effectiveness and market penetration cap (the latter being the cap of sales for each vehicle model of a vehicle type that can receive a technology package) T); 3) Data the model uses to calculate technology costs, such as the package cost and cost learning coefficients 4) Properties of the technology package, such as refrigerant type and fuel type.

The “TechPacks” tab in the Technology input file contains a us er-defined list of technology packages for all of the different vehicl e types. This list is organized in ascending order by the vehicle type to which it applied (Column A), and then by ascending technology package (Column B). In order to avoid the comp lexity of synergistic effects, the user must record the technology packages with in each vehicle type from top to bottom in the order of how the model should add them to the particular ve hicle. Since the user defines the technology packages contained in the input file, technol ogies are dynamic (user defined) and not “hardcoded” within the model. The user records a description of the technology package in Column C, and an abbreviation, if desire d, in Column D. The abbreviati on of the last technology package added is displayed in the output files, and is useful in dete rmining the technology penetration results via array math.

Columns E-L contain the market penetr ation caps for the technology packages; each column in alphabetical order represents ascen ding redesign cycles. When technology is sufficiently new, or the leadtime available prior to the end of the redesign cycle is such that it is not reasonable to project that it could be applied to all vehicle models that are of the same specific vehicle type, for example, all minivans, the user can limit its application to minivans through the use of a market cap of less than 100% in columns D through K. This cap can vary by redesign cycle. When a technology package is ap plied to fewer than 100% of the sales of a vehicle model due to the market cap, the effec tiveness of the technology group is simply reduced proportionately to reflect the tota l net effectiveness of applying that technology package to that vehicle’s sales. OMEGA does not create a new vehicle with the technology package and retain the previous vehicle which did not receive the technology package, splitting sales between the old and new vehicles. If subse quent technology packages can be a pplied to the vehicle, the user should consider whether in realit y the new technology would likely be applied to those vehicles which received the previous technology or those which did not, or a combination of the two. The effectiveness of adding the subsequent technology may depend on which vehicles are receiving it.

Values in columns M through T contain the Av erage Incremental Eff ectiveness (AIE) of the technology package over the previous package (or over the baseline in the case of technology package 1) which the user can adjust on a redesign cycle basis. For exam ple, a value of 7.0% in the AIE column would denote a 7.0% reducti on in tailpipe CO2 em issions beyond any CO2 reductions already realized. (“Tailpipe CO2” re fers to the CO2 emitted over the test cycle used

13to determine compliance with the CO2 em ission st andard being modeled.) This value of 7% would include any synergistic effects that com ponents of the technology package may have with technologies that have already been added to the vehicle type.

The user should be aware that many CO2 em ission control technologi es affect vehicle attributes, such as weight, size and performance. For example, turbocharging always increases peak engine power and thus, performance. Dies elization also tends to increase vehicle weight and depending on the size and aspiration of the en gine, can also impact performance in either direction. A common way to specify a technology ’s CO2 effectiveness is to do so holding other vehicle parameters, such as utility and performance, as constant as possible. It may be impossible to hold weight constant . In this case, the impact of the change in vehicle weight should be included in the specified effectiveness of the technol ogy. However, the specification of technologies, thei r effectiveness and th eir impact on other vehicle para meters is up to the user. We simply recommend that the user indicate when the application of a given technology is expected to affect vehicle parameters wh ich may affect consumer desirability.

Column U contains the refrigerant effectiven ess and is based on the fraction reduction in direct refrigerant leakage emissi ons and is separate from tailpipe CO2. Based on a change in refrigerant included as part of a technology packag e, noted in Column V, the model will convert the refrigerant emissions to grams per mile of CO 2 equivalent emissions, and add the resultant to the tailpipe CO2 emissions for th e vehicle compliance calculation. A value of “NC” in column denotes that there is “No Change” in refriger ant from the previous st ep, or in the case of technology package 1, no change fr om the baseline refrigerant.

Column W describes the primary fuel or energy source utiliz ed by the technology package. Acceptable values are G for gasoline, D for Diesel, or EL for electricity. If a vehicle model has two fuels, OMEGA assumes that the vehicle is a plug-in hybrid electric vehicle (PHEV), and electricity is the secondary fuel. T hus, for the case of PHEVs, the user must enter either “G” or “D” into the primary fuel column. If a vehicle model is run on electricity only (an EV), the primary fuel can be designated as “EL”.

Column X contains the level of electric ity use existing after application of the technology. While base electricity use can vary by vehicle, electric ity use after the application of technology is done at the vehi cle type level. The fact that the vast majority of current vehicles do not use electricity prevents us from specifyi ng post-technology electricity use in terms of a reduction from the baseline usage. The inability to represent slight differences in electricity use between specific vehicle models is not too restric tive, given that no data are available to indicate the degree of these differences. For example gi ven that manufacturers do not currently produce a wide range of plug in and fully electric vehi cles, there is no basis to project that one manufacturer’s mid-size electric cars will use 5% more electricity per mile than those produced by another manufacturer.

Column Y contains the incremental technology package cost, which represents the cost beyond the cost of other technology p ackages that the model may have added in a previous step. In the specific case of technology package 1 on each vehicle type, th is will be the package cost over the baseline, since no other technology ha s been added prior to technology package 1.

14C. The Fuels File: Physical Properties and Prices of Fuel and Energy Sources

The Fuels input file contains data relevant to fuel and electricity, including energy, mass, and carbon density, and annual price fo recasts for up to 20 years. Sim ilar to the other input files, the fuels input file is comprised of two work sheets: A worksheet containing data used by the model and an additional worksheet designed to ensure that the data is within range. There is a third hidden worksheet which contains the rang e values. The following table describes the layout and content of the “fuel” tab in the fuels input file.

Table II-3 Input Data in Fuel Worksheet of the Fuels File
Column(s) Contents

A Fuel Type B* Energy Density* C* Mass Density* D Carbon Density E - X Fuel Price ($/gal) *The columns highlighted in gray are not used by the model’s core calculations.

Column A describes the fuel type. Currently, this value is limited to G for gasoline, D for Diesel, or EL for electricity. In the future, EPA plans to increase the model’s capability to handle additional fuels, such as E85 and low sulfur gasoline, for example.

Column B contains the energy density of the en ergy source. For the liquid fuels, the units are BTU per gallon. For electric ity, the units are BTU per kilowa tt-hour. Column C contains the mass density of the liquid fuels in terms of grams per gallon. It has no value for electricity. Both Columns B and C are not used in the model’s core algorithm.

Column D describes the carbon density of the energy source, and is used in the calculation of the Technology Appl ication Ranking Factor (TARF) , described in the section on the modeling process and algorithms. This is in units of grams of carbon per gram per the unit of fuel which is associated with the fuel price. For liquid fuels, this is grams of carbon per gallon and for electricity this is gr ams of carbon per kWh. Since ma nufacturers do not consider the upstream emissions of electricity when addi ng fuel-saving technologi es to vehicles, EPA currently records the carbon density of electricity as 0. If the ag ency chooses in the future to incorporate the upstream emissions of carbon in the TARF calculation, EPA could enter in a generally accepted average valu e of 853 grams of carbon per kWh of electricity produced (or other value).

Columns E through X contain the fuel price eith er in US Dollars per gallon or US Dollars per kWh for liquid fuel and electr icity, respectively. Fuel prices are displayed in ascending order by year and are used when calculating the fuel savings over the payback period in the TARF.

D. The Scenario File: Definition of Regulatory Scenarios and Other Economic Parameters

15The Scenario input file contains data specifying the numbe r and types of model runs. The Scenarios tab acts as a director y for different model runs, where the user can create an entry for any number of runs that the model can perf orm in succession. In the Scenarios tab, the user must specify the base year, type of complian ce target (CO2 or MPG), type of compliance function, the number of redesign cycles, and the na mes of the other input files that describe the vehicle fleet, technology packages, and fuel propertie s. At present, the model is limited to a CO2 equivalent standard. The elements in the “Scenar ios” tab described above are summarized in the following table:

Table II-4 Input Data in Scenario s Worksheet of the Scenario File
Column(s) Contents

A Scenario ID number B Scenario name C Baseline year D Target Type E TARF Option F GHG Target Function Type G Fleet type (single or combined) H Number of redesign cycles I Fleet trading limit J Market input file indicator K Technology input file indicator L Fuels input file indicator M Reference input file indicator

Column A in the Scenarios tab indicates the ID number of the model run. The model can run as many different scenarios as the user desi res automatically in succession. In Column B, the user can give each scenario a name, which the model will use in naming the output files for each scenario.

Column C denotes the baseline year for each sc enario. This is the year prior to the first year of the redesign cycle.

Column D indicates the type of standard or target the model will use for compliance. Currently, the only available opti on is CO2, although it is conceiva ble that EPA may incorporate MPG as an additional target metric in the future.

Column E is where the user designates which of the three Technology Application Ranking Factors (TARFs) the model should use. The model uses the TARF equations to determine the order of technology package application on the differe nt vehicle types. There are three TARF equations which are summarized qualitatively below.

1. Effective Cost = [Technology package cost minus fuel savings over the payback period minus the implicit reduction in fines for non-compliance]. 2. Cost Effectiveness-Society = [Effective Cost] divided by discounted lifetime CO2 emissions.

163. Cost Effectiveness-Manufacturer = [E ffective Cost] Divided by lifetim e CO2 emissions.

These 3 TARFs are described in detail in Section III.C below.

Column F indicates the user’s preference fo r type of function fo r the industry-wide GHG level. There are three options for compliance targets: 1 = universal; 2 = piecewise linear; 3 = constrained logistic. The unive rsal target option is simply a numerical designation which the manufacturers’ average fleet CO2 cannot exceed. In contrast, the attribute-based linear target function is described by up to four coeffici ents and has the following piecewise linear mathematical form:

Where

      

 

  



max max min min max min

; ;*] [ ;

FPFPB FPFPFPAFP

FPFP AB FPFPA

y A: Minimum level of CO2 target B: Maximum level of CO2 target FP min : Footprint value where CO2 target

reaches minimum value (i.e., intersection of lowe r asymptote with slope) FP max : Footprint value where CO2

target reaches maximum value (i.e., intersection of uppe r asymptote with slope) FP: Vehicle footprint (square feet)

17Example of Footprint-Based CO 2 Piecewise Linear Target Function

Footprint (ft 2 )CO2(g/mi)

The footprint-based logis tic curve (shown below) is describe d by four coefficients and has the mathematical form described below.

Where A, B, and FP have the same definitions as above C: Footprint value at equation inflexion

point (i.e., m idpoint) D: The term which controls the rate at

which the CO2 target moves from the minimum to maximum values. High values of D cause the curve to move slowly (in terms of footprint) from the minimum to maximum target (i.e., a sort of “inverse slope”) 

##     

 

  

 

 

 

D CFP D CFP

e e ABAT
1

FP: Vehicle footprint

18Example of Footprint-Based CO 2 Logistic Target Function

Footprint (ft 2 )CO2(g/mi)

Column G of the Scenarios tab in the Scenario input file is entitled “fleet type”, and EPA plans to update this title in a future version. There are two acceptable va lues for cells in this column: 1 = 1 fleet and 2 = 2 fleets. Originally these options were designed to indicate whether the car and truck fleets were combined or separate . However, since the user can run a combined fleet with just cars, just trucks , or cars and trucks combined, EPA plans to change the name of column G to either “Number of fleets” or “Number of standards” for clarit y. If 1 is chosen, the standard defined in the “Standards” tab will be ta ken from the “Car” standa rd section, whereas if 2 is chosen, the standards will be defined by both the “Car” and “Truck” standard sections.

Column H contains the number of redesign cycles the model considers in a given run. There can be as few as one redesign cycle and as ma ny as eight. The length of redesign period is currently hard coded to be 5 y ears, although EPA may make this an editable input in a later version. Thus, in its current state, the model can analyze standards up to 40 years into the future.

Column I contains the limit (in units of g/ mi CO2) imposed on the trading of compliance credits between cars and trucks. One of the fl exibilities proposed in EPA’s recent vehicle GHG rule is trading across a manufacturer’s car and truck fleets. As described in the proposed regulations, trading of credits would occur in terms of tons of CO2 emissions. Credits are generated when a manufacturer’s fleet of either ca rs or trucks over complie s with the applicable

19standard. The amount of credits generated equa ls the difference between the sales weighte average emission lev el for that vehicle class a nd th d e applicable standard multiplied by the fetime VMT per vehicle for that vehicle class. li



##  


##  





  

 CorT

v v v CorT

v v v v v

LifeVMT Sales etTTCO LifeVMT sSale rated CreditGene

1 1 arg2(

Where Sales v is the sales volume of each car or truck in that redesign cycle, LifeVMT v is the lifetime VMT per vehicle, which is th e sum (over the life of the vehicle) of th product of the vehicle survival percentage by vehicle age and the a nnual VMT per vehicle by vehicle age. Both sets of these values are input to the model in the Reference file. This value i the same for all cars (an e

s d all trucks) and coul d effectively be moved outside of the summation

alent CO2 of each car or

F and the numerical inputs described on the Ta rget worksheet of the cenario file (see below).

la, except for the substitution of the ehicles from the other vehicl e class, as shown below. portion of the formula, TCO2 v is the sum of tailpipe and refrigerant emissi ons in terms of equiv truck after the model has determined that compliance is achieved, and Target v is the emission standard or target applicable to each car or truck, as defined by the type of target defined in column S

Credit use is determined by the exact same formu

v


##  


##  





   

 CorT

v v v CorT

v v v v v

LifeVMT Sales TCOetT LifeVMT sSale CreditUsed

1 1 2 arg(

the target applicable to each car and truck by both sales and fetime VMT, as shown below. The model implements this trading scheme by determining the sta ndard applicable to both cars and trucks by weighting li



##  


##  





  

 V

v v v V

v v v v

LifeVMT Sales etT LifeVMT sSale
etTO FleetwideC

1 1 arg
arg 2

Fleetwide total tailpipe and refrigerant CO2 equivalent emissi ons are determined in an analogous

shion, as shown below. fa

20

##  


##  





  

 V

v v v V

v v v v

LifeVMT Sales TCO LifeVMT sSale
CO FleetwideT

1 1 2
2

Compliance is achieved when FleetwideTCO2 is equal to or less than FleetwideCO2Target.

and

so ance. The lgorithms which accomplish these tasks are described below in Section III.A.

put ntains the name of the fuels input file; column M contains the name of the eferences file.

s to

contained in the “Economics” tab and is orga nized as illustrated in the following table:

Table II-5 ata Contained in the Ec onomic the Scenario File
Colu (s) Inputting zero in Column I prevents such tr ading and requires manu facturers to comply with the relevant em ission standard s for their cars and its trucks separately. If the user desires to allow unlimited trading, this is accomplished by inputting a large non-zero number in Column I (e.g., 100 g/mi CO2). This effectively allows th e model to apply techno logy across both cars trucks and meet the combined standard descri bed above. We say “effectively”, because the model still checks to ensure that the trading limit is not triggered and this cannot be definitively determined until compliance is achieved at the end of the evaluation of this manufacturer. Thus, in addition to requiring compliance with the flee twide average CO2 target, the model must al ensure that each vehicle complies with its applic able target plus the trading allow

a

Columns J-M indicate which files the model will access for the different runs. Column J contains the name of the market input file; column K contains the name of the technology in file; column L co R

The Scenario input file also contains the economic parameters that the model use calculate the Technology Application Ranking Factors (TARFs).Such economic data is

Input D s Worksheet of

mn Contents

A Identi l run fication of mode B Discount rate C Pa d yback perio D CA e FE fin E Gap F Threshold cost G CO2 value increase rate

Column A contains the ID numb er for the model run, and it mu st be identical to that in Column

he TARF calculations when comparing technology package and vehicle model combinations. A of the Scenarios tab.

Column B contains the discount rate for future dollar values. The model uses the discount rate in t

21Column C designates the payback period, which the model uses to consider the length of time which consum ers consider fuel sa vings when purchasing a vehicle.

l the im pact of a fine, such as the CAFE fine. OMEGA uses this value in the TARF equations.

orld

ad

fuel savings in the core model’s TARF equations, which are defined in Section III C, below.

el

of

ow the that technology at the chnology penetration cap for that technology.

hout,

es, the model will flect these two approaches to technology application on the part of a manufacturer.

ll

used in the Cost Effectiveness-Manufactur er and Cost Effectiveness-Society ARFs described below.

olumn

e Column D indicates the value of a fine for non-compliance. Since the Clean Air Act does not allow EPA to charge a monetary fine for non- compliance, EPA typically records $0 into this column. However, there are instances when EPA would like to mode

Column E contains the Gap, wh ich is the difference between test cycle and real-w CO2. This value is important because manuf acturers consider the test cycle CO2 when complying with a CO2 standard, whereas the tr ue impact on climate is based on the on-ro CO2. The model uses this difference to conve rt test cycle CO2 emissions to onroad fuel economy and calculate

Column F contains the value for the “threshold cost”. This value determines whether the mod will accept some degree of over-com pliance when it determines the degree of technology addition needed for a manufacturer to meet a standard, or if it will reduce th e addition of technology so that a manufacturer just barely complies. Specifically, if the per vehicle co st of the last technology added by the model in order to reach compliance exceeds the thre shold value, the model re duces the percentage that vehicle’s sales which receives that technology to just the degree needed to en able compliance. If the per vehicle cost of the last technology added by th e model in order to enable compliance is bel threshold value, the model leaves the percentage of vehicle sales receiving
te

This flexibility was included in the model to reflect the different ways in which manufacturers apply various technologies. For example, when a dding basic engine technology such as variable valve timing, the manufacturer would generally convert the entire production volume of a specific engine to this technology. The production of two different e ngines, one with the te chnology and one wit would not be maintained. However, with more extreme technologies, such as dieselization or hybridization, the manufacturer often maintains two versions of the ve hicle, one with and one without these technologies. By setting the threshold in be tween the costs of these two exampl

re

Column G represents the rate of increase in th e real value of CO2 emi ssion reductions over time. This value is included since some studies have pred icted that the value of CO2 emission reductions wi increase over time as the impact of global climate cha nge increase. The units of this term are percent per annum. This value is T

The Target tab of the Scenario file contains the parameters describing the GHG compliance targets required by the universal, segmented lin ear and constrained logistic func tions described above. The first 32 columns describe the car standards for up to eight rede sign cycles, if “2” fleets are selected in c G of the Scenarios worksheet. If “1” fleet is se lected, then the standard s described in Columns A through AF apply to both cars and trucks. Similarl y, the next 32 columns (AG through BL) describe th

22truck standards for up to eight redesi gn cycles if “1” flee t is selected. These valu es are not used if “1” eet is selected. This is de scribed in Table II-6 below.

Ta Input Data Contained in the orksheet of the Scenario File
Co ) Vehicle Class fl

ble II-6. Target W
lumn(s Contents

A - D Coefficients describing the CO2 Standard for Design Cycle 1 E – H Coefficients describing the CO2 Standard for Design Cycle 2 I – L Coefficients describing the CO2 Standard for Design Cycle 3 M – P Coefficients describing the CO 2 Standard for Design Cycle 4 Q – T Coefficients describing the CO2 Standard for Design Cycle 5 U – X Coefficients describing the CO2 Standard for Design Cycle 6 Y - AB Coefficients describing the CO2 Standard for Design Cycle 7 AC - AF Coefficients describing the CO 2 Standard for Design Cycle 8 r If “2” flee Ca ts

Ca 1” fleet selected selected r and Truck if “

AG – AJ Coefficients describing t he CO2 Standard for Design Cycle 1 AK – AN Coefficients describing the CO2 Standard for Design Cycle 2 AO – AR Coefficients describing the CO2 Standard for Design Cycle 3 AS – AV Coefficients describing the CO 2 Standard for Design Cycle 4 AW – AZ Coefficients describing t he CO2 Standard for Design Cycle 5 BA – BD Coefficients describing the CO2 Standard for Design Cycle 6 BE – BH Coefficients describing the CO2 Standard for Design Cycle 7 BI - BL Coefficients describing the CO 2 Standard for Design Cycle 8 Truc ets

Not u fleet selected k If “2” fle selected sed if “1”

As indicated, each CO2 standard is described by up to four coefficients. For a universal standard only the value listed in the first of each set of four columns is used (Labeled “A” in the header ro the Target worksheet). All four columns are required to describe either the segmented linear or constrained logistic functions desc ribed above. The four columns in each set of four columns describe the coefficients labeled A, B, C, and D, respectiv ely, in the segmented linear and constrained logistic , w of

nctions described above. The header row of the Target worksheet i ndicates these coefficient labels.

E. The Reference File: Vehicle Survival Rates and Miles Driven

EGA’s compliance calculations as it pertains to credit trading within a manufacturer’s cars and trucks.

lumns D and E contain the average miles driven annually (per vehicle) for cars and trucks accordingly.

. “Hard-coded” Input Data fu

The References file contains all data which re fers to car and truck annual miles driven and survival rates by year. This data is used in all TARF calculations when determining fuel savings over the payback period and when determining the life time CO2 reduction (in TARFS 2 and 3 only). The data in this file is also used in OM

Data which OMEGA uses in its calculations is contained in the tab entitled “Vehicle Age”. Column A in this tab contains the vehicle age in years. Columns B a nd C contain car and truck survival rates, respectively. This is the percent of cars and trucks which remain on the road each year. Co

F

23The only values hard-coded into the model wh ich are potentially used in the application of technology to vehicles are th e global warming potentials and th e distribution of leakage r by vehic ate le age. These are only used if refrigerant em issions are included in the market file. At resent, acceptable refrigerants are listed be low in the following table with their respective GWPs.

t GWP p

Refrigeran HFC-23 1 1700 HFC-125 2800 HFC-134a 1 300 HFC-143a 3800 HFC-152a 140 HFC-227ea 2900 HFC-236fa 6300 HFC-4310mee 1300

The following table describes the refrigera h are currently hard-coded.

rig

Fr nt leakage ra tes whic

Ref erant Leakage actions

year % of T etime Leak ear otal Lif age g/y
1 1.1% 2 1.5% 3 2.0% 4 2.4% 5 2.9% 6 3.3% 7 3.6% 8 4.4% 9 5.5% 10 6.9% 11 8.5% 12 8.1% 13 7.5% 14 7.0% 15 6.5% 16 6.0% 17 4.8% 18 3.9% 19 3.3% 20 2.7% 21 2.3% 22 1.8% 23 1.4% 24 1.1% 25 0.9% 26 0.7%

2427 0.0% 28 0.0% 29 0.0% 30 0.0% 31 0.0% 32 0.0% 33 0.0% 34 0.0% 35 0.0% 36 0.0%

EPA plans to make this data editable in a futu re version, such that the name and GWP of each refrigerant can be changed as new refrigerants are developed over time. Other information such as of upstream criteria pollutant emissions from fuel/energy production, st orage, and dist regression coefficients for downstream criteria po llutant emissions, emissions damage costs, and externalities related to crude oil importation and use, externalities related to driving are automatically loaded into the be ribution,

nefits calculation spreadsheet, where the user can edit the values, desired. The user can change the value of these factors in the spreadsheet if the use of

G.

in
prove

a ta for both sets of in put files is the same; rather, it is EPA’s use and presentation of th e data which have evolved over time, as new version

se for

rence is that the Market file distributed with the installation file has ma nufacturer distinctions removed, while t

EPA’s methodology for creating the Technology file is described in detail in the joint DTSD, Chapte -003)

s files is the Energy Information Adminstration’s Annual Energy Outlook publication for 2009, the “Stimulus ” edition. This file is also a duplicate of the o if al ternative factors is desired.

Input Files Currently Being Distributed with Model

The sample files bundled with th e installation file closely re semble those used by EPA the EPA-NHTSA joint proposed rulemaking to es tablish greenhouse gas standards and im fuel economy for motor vehicles. The underlying d

s of the model have become more capable.

The data contained in these files are from se veral different sources. In the Market file, the baseline vehicle models and characteristics we re taken from EPA’s certification databa model year 2008. Projected sales were determin ed using a complex mapping process between EPA’s certification database and data purchased from CSM International. This process is described in detail in Chapter 1 of the EP A-NHTSA joint Draft Technical Support Document (DTSD) to the Agencies’ joint rule (Document No. EPA-420-D-09-001). The only diffe

hat used in support of the proposed rule retained the manufactur er distinctions.

The Technology file is an exact duplicate of the one used in the rulemaking proposal.

r 3, and EPA’s Draft Regulatory Impact Analysis (DRIA) Chapter 1 (EPA-420-D-09

The source of the data in the Fuel

ne used in EPA’s proposed rule.

25The economic data provided in the sample Scenar io file is exactly the same as that used for the proposed rule. However, because the vers ion of OMEGA used for the rule did not have a car-truck trading algorithm , EPA set the complia nce target coefficients differently in the rulemaking Scenario file to determine the tec hnology cost using an alte rnative approach. The sample Scenario file describes two separate s cenarios using the proposed regulatory coefficients: Scenario 1 comprises the exact coefficients resulting in a 250 g/mi CO2 fleetwide average and Scenari g

nd NHTSA for the rulemaking, and a detailed description of this data is contained in the DTSD, Chapter 4. The underlying data

r the two files are the same, but were adjusted downward for Trucks in the rulemaking version, ternative car-truck trading algorithm.

he rigerant emissions speci fied for each vehicle into its /mi CO2 equivalent and adds this to the baselin e tailpipe CO2 emissions for each vehicle. The d se een pplied. These steps are described below. o 2 coefficients are relaxed to reflect the approximate 11 g/mi credit for air conditionin technology expected to be uti lized in the 2016 model year.

The inputs in Reference file were deve loped by EPA a

fo to accommodate the al

III. Model Operation

The operation of the “core” model can be described in three steps. The first step determines the effective CO2 emission standard for each manufacturer and vehicle class. T second step converts the baseline lifetime ref
g thir tp applies technology until this standard is met or all available technology has b

a

A. Determination of Manufacturer-Specific CO2 Emission Standards

As described above in Section II.D., thr ee types of standards can be evaluated by OMEGA: universal or flat, segmented linear, and constrained logistic. Determining each manufacturer’s effective standard under a flat standard is simple; it equals the flat standar second two types of standards requ ire sales-weighting the CO2 target applicable to each specifi vehicle model produced by that manufacturer. Th e model calculates each vehicle’s CO2 target by plugging the vehicle’s footprin t into either the footprint- based formula describing the standard. Each of the vehicle-specific targets are then multiplied by the vehicle’s sales in th redesign cycle and the lifetime VMT for that vehi cle class and summed across al d. The

c

at l vehicles. The m is then divided by the sum of the product of vehicle sales by that manufacturer in that

is

s and one for trucks, which will likely differ due to difference between car and truck sales, lifetime VMT and footprint va lues, if applicable. Howev su redesign cycle and the lifetime VMT for each vehi cle. The result is essentially a sales and lifetime VMT weighted corporate average CO 2 target for each manufacturer.

If separate standards are specified for cars a nd trucks, then the model performs this task twice for each manufacturer for each redesign cycle being modeled: on ce for cars and once for trucks. If the same standard is specified for both cars and trucks and two compliance fleets are specified in the Scenario file, th en two separate calculations ar e still performed. The result still two distinct CO2 em ission targets, one for car

er, if one fleet is specified, then th e model only performs this calculation once and includes both cars and trucks in the calculation.

26If a trading limit of “0” is specified, then this stage of the model opera tion is comp lete a non-zero trading limit is specified, an additional step is required to ensure that this trading limit is not exceeded. Conceptually, when trading is allowed and pr actically limited, a manufacturer must comply with three distinct standards, one for cars equal to its standard for cars plus the trading allowance, one for trucks equal to its standard for trucks plus the trading allowance, the sales and VMT weighted standard for cars an d trucks combined. Conceptually, we assum that a manufacturer would prefer to evaluate th e application of technology to both its cars and trucks at th . If

and

e

e same time in order optimize co mpliance (via the TARF) over the widest set of vehicles. At the same time, the manufacturer is assumed to want to avoid generating credits from on n

single d

VMT weighted average emissions for either cars or tr ucks reaches this lower limit, no additional technology is applied to that veh

nput and the applicable car and

uck standards for a manufacturer are 220 a nd 300 g/mi CO2, respectively. The model calculates the combined sales and VMT weighted CO2 emission target using the above equation for FleetwideCO2Target, which can be simplified as follows: e of its two vehicle class fleets which can not be used by the other fleet due to the limit o trading.

OMEGA utilizes a simple set of algorithms to achieve both of these goals with a set of technology applications. It does so by calculating the level of sales and VMT weighte emissions of either car and truck emissions which will generate the maximum allowable use of credits by the other vehicle class. It then tracks sales and VMT we ighted car, truck and combined emissions when adding technology. When the sales and

icle class. Technology is on ly applied to the othe r vehicle class, as th is latter class still exceeds the level of its standard plus the allowable trading level.

For example, assume a trading limit of 10 g/ mi CO2 is i
tr



##  


##  

2

v Sales 

## 



  



1vc v 2

1vc v v v

LifeVMT Target LifeVMT sSale O2Target FleetwideC

same

truck emissions which produces useful credits for use towards co mpliance by cars is determined which achieves compli mi Where vc represents the two vehi cle classes, cars and trucks.

Once FleetwideCO2Target is known (assumed to be 260 g/mi in this example), this equation can be used to determine the level of average car emissions which achieves compliance when the level of average truck emissions exceed its target by 10 g/mi CO2 (310 g/mi in this example). For this example, assume this le vel is 198 g/mi CO2. Any level of average car emissions between 198 and 220 g/mi produces credits for tradi ng which can be effectively utilized towards compliance by trucks. Likewise , the minimum level of average

ance when the level of average car emissi ons exceed its target by 10 g/mi CO2 (230 g/ in this example). Again, for this exampl e, assume this level is 302 g/mi CO2.

The model then proceeds to add technologies to cars and trucks (described in greater detail below) until one of three conditions is met: 1) combined car and truck emissions reach 260

27g/mi CO2, 2) car emissions reach 198 g/m i CO2, or 3) truck emissions reach 302 g/mi CO2. If combined car and truck emissions reach 260 g/mi CO 2 before the other two criteria are met, the evaluation of this manufacturer is complete. The manufacturer complies with the sales and weighted standard applicable to cars and trucks combined and any level of trading implied is less than the maximum allowed. If criterion 1 is reac hed first, the model continues to evaluate and apply technology to both cars and trucks. Howe ver, if the TARF indicates that technology should be applied to a car next, the model assumes that that ve hicle already has that technology present on it, retains its cumula tive technology cost and emission level and moves to the next technology. In this case, the model continues to effectively add technology to trucks only until criterion 3 is met (unless insuffi cient technology exists to enable overall compliance). L if criterion 2 is reached first, the model conti nues to evaluate and apply technology t VMT

ikewise, o both cars nd trucks. However, if the TARF indicates th at technology should be applied to a truck next, mod umulative technology cost and emission leve l and moves to the next technology. a the el assumes that that vehicle already ha s that technology presen t on it, retains its

c

B. Converting Lifetime Refrigerant Emissions to CO2-Equivalent emissions per Mile

Refrigerant leakage emissions are input to th e model in terms of lifetime emissions, while Refrigerant leakage increases with wear and tear of the A/C system. Leakage emissions also occur during accidents, repairs and vehicle scrappage. Refrigerant emissions per year increase significantly with age. Since a nnual mileage decreases with age in general, refrigerant emis sions

er mile increase even more significantly with age than emissions per year. In contrast, CO2

2

. er mile, which, if constant over the vehicle’s fe, like tailpipe CO2 emissions are generally assumed to be, would produce the same societal missions over the life of the vehicle. This equality is shown in the followi

Value of Equivalent Lifetim e CO2 Tailpipe Emissions = Value of Lifetime Refrigerant Emissions

where V ction for a vehicle of age i tim es the annual vehicle miles driven for a

V hen the vehicle

is at age i, p tailpipe emissions are input in terms of g/mi and are generally assumed to be constant with age. Thus, some processing is needed to be the two types of emissions on a comparable basis.

OMEGA converts lifetime refrigerant emissions to their equivalent value in terms of CO emissions per mile (RCO2) considering the change in societal value of CO2 emissions over time The goal is to estimate a level of CO2 emissions p li value of CO2 emissions as the distribution of refr igerant e ng equation:

]2 *e [RefLeakag *]2 *[*2 36

1 36

1 i ValueofCO GWP ValueofCO VMT RCO

i i i i 

##  



 

i

MT i is the annual miles travelled for the relevant class of ve hicle at age i, (i.g., VMTi =

Survival Fra vehicle of age i as input in the Reference file), alueofCO2 i is the societal value of CO2 emissions in the calendar year w

RefLeakage i is the annual rate of refr igerant emissions when the vehicle is at age i, and

GWP is the global warming potential of the refrigerant relative to CO2.

28The value of CO2 in a particular year can be described as its ba se value divided by one plus the applicable discount rate raised to the power of the age of the vehicle. In this case, the applicable discount rate is the soci etal discount rate less the rate of increase in the real value CO2 em is of sions. Since the normal discounting equa tion assumes that the re levant activity (e.g., payment) occurs at the end of the year and emissions occur throughout the year, we multiply by one plus half of the applicable annual discount rate. The resulting equa tion is described as follows:



###      

 

## 

 ] 1 2 1 2 *[*)(2 * 36

1 / i i i IRDR IRDR
fCO BaseValueo VMT RCO mig



##   ] 1 2 2 *e[RefLeakag * *

1 i i i 

### IRDR fCO BaseValueo GWP
 1 36 

### IRDR

 

This equation can be rearranged to solve for the level of CO2 equivalent emissions per mile as shown in the next equation. (Note that the societal valu e of CO2 in the base year falls out of the equation.) 

###  

# 





###  

  

## 

          

1 1 i i i IRDR VMT

There are other approaches to converting lif etime refrigerant emissions to their g/mi equivalent level. Future vers ions of OMEGA may pr ovide the user with several approaches from which to select. One approach would be to simply allow the user to specify a baseline value for g/mi CO2 equivalent refrigerant emissions in the Market file. This way, the user co use whatever approach was deemed appropriate to estimate this value. Another approach wou be the same as the above, except to base the equivalency on physical emissions and remove the factors involving discounting. At the present time, the only use of the rate of increase in the societal value of CO2 emission reduc tions is in this calculation a nd as an input to the benefits calculation spreadsheet. Therefore, if the user desires to perform this equivalency using ph emissions, they should set the rate of increase in the societal value CO 2 emission reductions to equal the overall societal discount rate being specified. Th 

## 



   

      

 

   

 

  





36 36

1

2 1 1 2 1

)(2 / i i i

IRDR GWP IRDR IRDR

LeakRate

RCO mig

uld ld

ysical

is will cause all of the discount factors ation to be equa l to 1.0. Then, if the user desi res to use a different rate of ission reduc tions in the estimation of benefits, they can change this value in the benef its calculation spreadsheet. in the above equ increase in the societal value CO2 em

C. Application of Technology :

29The core model can mo st easily be described as the multiple application of thre steps. The first step is to determine the vehi cle in the “best” position to receive the next technological improvement to enable the manuf acturer to meet the specified CO2 emission standard. The second step is to apply th is technology, reduce the vehicle’s emissions accordingly and add the cost of this technology to the manufacturer’s total cost for this redesign cycle. e logical

The third step is to assess the complian ce situation and act accord ingly. If the standard pplies to cars only, trucks only, cars and trucks combined with a single standard, or cars and ., no trading), the model ill:

) recalculate the manufacturer’s sa les and VMT-weighted emissions,

on level to the manuf acturer’s standard,

,

the

3.a.ii) if the cost is above the threshold, the application of the technology (in terms of

d) if the manufacturer is still above the standard, the model goe s back to steps 1 and 2 above and app ions. If no more technology

ackages are available, the mode l moves onto the next manufacturer.

ecified, the model will:

nd both cars and trucks, combined,

.a) if the manufacturer is above all three criteria specified in Section III. A., the model goes back

truck standard but not below either e lower limit for generating useable tradable cred its for cars or trucks (i.e., meets criterion 3

cost specified,

3.b.i) if the cost is below the threshold, the evaluation of this manufacturer is over and the model moves onto the next manufacturer, a trucks combined with two distin ct standards and a zero trading limit (i.e

w

1

2) compare this new corporate average emissi

3a) if the manufacturer is now belo w the standard, determine if the cost of the last technology is above or below the threshold cost specified

3.a.i) if the cost is below th e threshold, the evaluation of this manufacturer is over and model moves onto the next manufacturer,

emissions and cost) is decreased until the standard is just met; the evaluation of this manufacturer is over and the mode l moves onto the next manufacturer,

3 lies another technology package to furthe r reduce emiss
p

If a non-zero trading limit is sp

1) recalculate the manufacturer’s sales and VMT- weighted average emissions for cars, trucks,

a

2) compare these three emission levels to the th ree criteria described in Section III.A. above,

3 to steps 1 and 2 above and applie s another technology p ackage to further reduce emissions. If no more technology packages ar e available, the model moves onto the next manufacturer.

3.b) if the manufacturer is now below the combined car and th specified in Section III.A , but not either criteri a 1 or 2), determine if the cost of the last technology is above or below th e threshold

30terms of tion of this anufacturer is over and the mode l moves onto the next m anufacturer,

.A.), de termine if the cost of the last chnology is above or below th e threshold cost specified,

3.c.i) if the cost is below the threshold, move to step 3.a.iii.,

s of emissions and cost) is decreased until the standard is just met;

packages if they apply to the vehicle class hose lower limit criterion was not met in step 3.a.i.

.A), determin e if the cost of the last technology is above r below the threshold cost specified,

old, the evaluation of this manufacturer is over and the to the next manufacturer,

terms of tion of this anufacturer is over and the mode l moves onto the next manufacturer.

for

n

uate the an outcome and determine if the inputs were reasonable or required odification. 3.b.ii) if the cost is above the threshold, the application of the technology (in emissions and cost) is decreased until the standard is just met; the evalua

m

3.c) if the manufacturer is now below the lower limit of either car or truck emissions which provides tradable emission credits, but not below the standard for cars and trucks combined (i.e., meets either criterion 1 or criteri on 2 specified in Section III
te

3.c.ii) if the cost is abov e the threshold, the application of the technology (in term

3.c.iii) Return to steps 1 and 2 above and apply another technol ogy package to further reduce emissions, but only effectively applying thes e

w

3.d.) if the manufacturer is now below the combin ed car and truck standard and below either the lower limit for generating useable tradable credits for cars or trucks (i.e., meets either criteria 1 and 3 or 2 and 3 specified in Section III

o

3.d.i) if the cost is below the thresh model moves on

3.d.ii) if the cost is above the threshold, the application of the technology (in emissions and cost) is decreased until the standard is just met; the evalua

m

Once all manufacturers are in compliance or have exhausted the technology available their vehicles, the model moves to the next re design cycle and repeat s the above sequence of steps. The evaluation of the second and any subs equent redesign cycles starts from scratch in that every vehicle begins with onl y those technologies pr esent in the Market file. In other words, the model does not start with th e technology added in previous re design cycles. The assumption behind this aspect of the model is that manufacturers have the fl exibility to reevaluate technology each time a vehicle is being redesigned. This could involve the evaluation of a technology which was not available in the prior redesign cycle or a technology which had a technology penetratio cap of less than 100%. Of course, starting the anal ysis over each redesign cycle could lead to situations where a manufacturer is projected to utilize several very different technologies for most of their vehicles in each of several redesi gn cycles. The user would have to eval reasons for such

m

31The steps outlined above are describ ed in more detail below. In the following formulae, the subscripts (t-1) and (t) indicate vehicle conditions before a nd after applying technology package “t”, respectively.

Determining the best application of technology: We have defined a metric called the Technology Application Ranking Fact or (TARF) to rank the applic ation of available technology packages across various vehicles. As descri bed above, the user can choose between three different TARFs for ranking techno logy application: 1) Effective Cost, 2) Cost Effectiveness– Society, and 3) Cost Effectiveness–Manufacturer. The first and third TARFs take the view of the manufacturer in determining th e application of technology. Th is can be deemed appropriate since the manufacturer controls the decision ma king process. Since the manufacturer must satisfy its customers and regulatory mandates, a manufacturer’s decision making processes will reflect these needs, as well. Mo re explicitly, the technology cost is assumed to be the full cost of that technology at the consumer le vel, including research and deve lopment costs, amortization of capital investment, etc. This co st is generally the same cost as EPA estimates in its regulatory support analyses when estimating the cost of new standards. 2 This cost is not necessarily the increment in price that the ma nufacturer would charge for th at technology, since price is a function of many factors which can change fair ly quickly depending on market conditions. The fuel savings are those assumed to be valued by the customer, so they are based on fuel prices including taxes and reflect the timeframe which a customer might consider when purchasing a vehicle. The residual value of the added technology is not curre ntly reflected in either TARF, but could be added in the future.

The second TARF takes the view of societ y in deciding which technologies should be added first. This distinction w ill be described further below.

In all three cases, the vehicle with the TARF with the most negative value is applied first. Because TARFs 1 and 3 reflect the manufacturers’ perspective, they are similar conceptually. Therefore, we describe bot h of these TARFs before describing TARF 2 below.

Effective Cost TARF (TARF No. 1): The Effective Cost TARF ( EffCost ) is defined as the cost of the technology ( TechCost ) less the discounted fuel sa vings over a specified payback period of vehicle use minus the reduction in CAFE non-compliance fee (FEE). The Effective Cost TARF was included in the OMEGA model, since it is the equivalent of the technology ranking process used in NHTSA’s Volpe Model. It allows a user to matc h this aspect of the Volpe Model when modeling equivalent standa rds using both models, if this is desired. Quantitatively, it is defined as follows:

       



  

 1 11 )1( 1

t t FCFC FEE GAP FS TechCost EffCost

2 See Section III.H of the Draft Regulator y Impact Analysis to the recent EPA NPRM for the control of GHG emissions from cars and light trucks for a discussion of technology costs at the manufacturer and consumer levels.

32EffCost is the cost of technology t per vehicle, as input in the Technology f ile. Fuel consumption per mile (over the compl iance test cycle used to determine compliance with the input CO2 emission standards) before (FC t-1 ) and after technology addition (FC t ) is calculated as follows:

     

 

 

 gC gCO CD CO FC

t t
t

12 244 2

1 1 1

     

 
gC gCO CD CO FC

t tTARF

t

12 244 2

,

Where CD t-1 and CD t represent the carbon density of the liquid fuel (in terms of grams carbon per gallon) before and afte r technology application, CO2 t-1 represents the level of tailpipe emissions prior to the applicati on of the technology being evaluated (i.e., after the applic ation of technology t-1), and CO2 TARF,t represents the level of ta ilpipe emissions after applica tion of technology t, without considering any cap on the level of the penetration of technology t.

CO2 t-1 is the actual level of CO2 emissions fr om the particular vehicle which would occur after the application of a ll the technologies prior to technol ogy t, considering both the fact that: 1) some or all of these te chnology packages may already be present on this vehicle (i.e., the TEBs), and 2) there may be caps on the application of one or more of these technology packages which are less than 100%. This level is the appropriate “baseline” CO2 level from which to evaluate technology t since all pr evious technologies must be added to the vehicle before technology t can be added.

As stated above, OMEGA takes a slightly different approach to calculating the CO2 emissions after application of t echnology t (for the purpose here of TARF calculation) in that we do not factor in the potential cap on the level of the penetra tion of technology t. At the present time, the sales volume of the partic ular vehicle is not factored in to the TARF. To factor in a percentage cap would be inconsistent. Certainl y amortized capital costs tend to be higher for technologies which are applicable to lower production volumes. However, the capital investment of many technologies can be spread across more than one vehicle model. Thus, incorporating the capital investme nt associated with technology into the TARF would not be a simple matter. If explicit consideration of capita l costs is added to the model’s operation in the future, both the cap on application of a particul ar technology and the sale s volume of the vehicle model will likely be relevant and included in the re vised TARF. The net result is that the TARF is essentially defined on a per vehicle basis.

Because the calculation of CO2 TARF,t does not consider the impact of any cap on technology penetration, this level of CO2 emissions will differ from the CO2 emission level of the vehicle should this technol ogy be selected for addition. The equation for the latter CO2 emission level is described further below.

33With these premises, the formulae describ i ng tailpipe CO2 emissions and refrigerant emissions after the application of this technology package are as follows:



##  

t t t t
t TARF TEBAIE AIE CO CO
  

  1 12 2 1

, 

##  

1 1

, 1 12 2

     



t t

t t t t tTARF GWP GWP
TEBAIE AIE RCO RCO

It should be noted that both e quations include the term (1-AIE t )/((1-AIE t *TEB t ). The effectiveness of adding the technology package (AIE ) is determined relative to a vehicle which does not have that technology package. If a ve hicle already has some aspect of the technology package present, its emissions are lower than th ey would be without this technology. The term in the denominator effectively increases the emi ssions of the vehicle to the level which would exist without the technology. The e ffectiveness of the technology p ackage can then be applied.

Fuel savings without consideration of th e gap between onroad and certification fuel economy (FS) is calculated as follows. Vehicles can have up to two separate energy sources, a liquid fuel (subscript “1” in the equation) and electricity (subscri pt “2” in the equation ), as would be the case with plug in hybrids.

] *) ( *) [(* 1)*5(,2 1,2 ,2 1)*5(,11,1 ,1
1 

 



 

  

##  iRCBY t t iRCBY t t PP FP FCFC FPFCFCVMTi i FS

Where i again represents vehicle age, VMT i represents annual vehicle miles tr avelled for a vehicle of age i, PP represents the payback period, or the length of time the user assume that consumers value fuel savings when purchas ing a new vehicle, stands FC represents fuel consumption per mile of e ither fuel 1 or 2, which is independent of both vehicle age and calendar year, FP represents fuel price of either fuel 1 or 2 in the calendar year equivalent to the vehicle age used to determine annual VMT. For i=1, th is calendar year is th e base year of the model run plus the redesign cycle times five years plus the vehicle age less one. For example, for a run with a base year of 2010 a nd the first redesign cycle, vehicles with an age of 1 are assumed to be driven in 2015, whic h is in fact the end of the first redesign cycle.

The GAP is the difference between real-world and test cycle fuel ec onomy. It is defined as one minus the ratio of on-road fuel economy to real word or on-road fuel economy. In terms of CO2 emissions and fuel consumption, on-road CO2 and fuel consumption equal test cycle CO2 and fuel consumption divided by (one minus the GAP), respectively.

In order to be consistent with the calculation of CO2 TARF,t , the technology cost ( TechCost ) is also determined without consideration of the presen ce of the technology on the

34vehicle (via the value of CEB) and without consideration of th e cap on the penetration of the technology. Thus, TechCost is simply the incr emental cost of the technology listed in the Technology file.

For current uses of the model, we recomm e nd that the CAFE fee be set to zero. CAFE fees do not apply under EPA CO2 emission c ontrols being considered and the above methodology can produce unreasonably high values for the reduction in CAFE fees in some circumstances. In this case, the Effective Cost TARF represents the difference between what it costs the manufacturer to add the technology to th e vehicle and the increase in value from the consumers perspective due to fuel savings. 3 A positive value for this TARF indicates that the consumer might view the vehicl e less desirably with the tec hnology than without. A negative value for this TARF indicates that the consumer might view the vehicle more desirably with the technology than without. This TARF assumes that manufacturers will incrementally add technologies which increase this desirability to the maximum extent possible.

Cost Effectiveness-Manufacturer TARF (TARF No. 3): The Cost EffectivenessManufacturer TARF ( CostEffManuf ) incorporates all of the terms included in the effective cost TARF, but also accounts for the degree that the technology brings the manuf acturer closer to its CO2 standard. It can be described simply as th e Effective Cost TARF di vided by the mass of the CO2 equivalent emission reduction over the life of the vehicle due to addition of the technology. The Cost Effectiveness-Manufacturer TARF can be described mathematically as follows:



#    

#        

        



 



   36

1

, 1

, 1
2 22 2 11 )1( 1

i i t tTARF t tTARF tt
t VMT CO CO RCO RCO FCFC FEE GAP FS TechCost uf CostEffMan

This equation can be rewritten more simply as follows:



#    

#        

 

  36

1

, 1

, 2 22 2

i i t tTARF t tTARF t VMT CO CO RCO RCO EffCost uf CostEffMan

Where, 

##  

1 1

. 12 2

   

t t
t tTARF GWP GWP RIE RCO RCO

The ratio of GWPs reflects the fact that th e technology might change the refrigerant used and affect the conversion of refrigerant ma ss emissions to CO2 equivalent emissions. All of the terms in the above equations have al ready been defined. The denominator could be described simply as the reduction in gram per m ile tailpipe CO2 and CO2 equivalent refrigerant emissions times the vehicle’s lifet ime VMT. For vehicles of the same class, VMTi is the same, so the inclusion of VMTi in the denominator ha s no practical impact. However, when cars and

3 Ibid

.

35trucks are being modeled together, the inclusion of th is term has an effect. Its effect is consistent with EPA’s proposed car-truck tr ading provisions includ ed in its recent vehicle GHG proposal.

As described above for the Effective Cost TARF, we recommend that the CAFE fee be
set to zero w ith this TARF. The Cost Effectiv eness-Manufacturer TARF represents the degree to which consumer desirability might change with the technology on a per ton of CO2 controlled basis. The Effective Cost TARF ignores the degree to which the technology moves the manufacturer closer to compliance. Thus, a fairly expe nsive technology which produces a large fuel savings might have a fairly large negative Effective Cost TARF. The sequence of several less expensive technologies with smaller fuel sa vings might all have less negative Effective Cost TARFs and so would not chosen over the more e xpensive technology. However, in total, the sequence of smaller steps might achieve the same overall emission reduction as the more expensive technology at a lower cost. The Co st Effectiveness–Manufacturer TARF would divide these less expensive tec hnologies by smaller CO2 emission reductions, thereby increasing their TARF values relative to the more expensive technology. This would allow the model to choose the sequence of less expens ive technologies over the single expensive technology if their increase in consumer desirability pe r ton of CO2 reduced was higher.

Cost Effectiveness-Society TARF (TARF No. 2): The Cost Effectiveness-Society TARF ( CostEffSoc ) is identical to the Cost Effectiveness–Ma nufacturer TARF with one exception. The Cost Effectiveness-Society TARF divides the Effective Cost TARF by the discounted lifetime CO2 emission reduction instead of the lifetime CO2 emission reducti on. The discount rate used would be that for the value of CO2 emission reduc tions over time. The rationale for discounting is simply that society is presumed to value CO2 reductions in this way. Also, in this case, we would recommend that the user increase the payback period for fuel savings to cover the life of the vehicle instead of the much shorter peri od of time often assumed to be considered by consumers when purchasing a vehicle. Society va lues fuel savings over th e life of the vehicle and not just for the first few years of use. Th e user could also estimat e technology costs from a societal point of view if this produced different cost estimate s than those used above. The Cost Effectiveness- Society TARF can be described mathematically as follows:



#     

#     

#     

 

   

 

  

 

  

 

  



 

 

  36

1

, 1

, 1 2 1 *2 22 2

i i i t tTARF t tTARF t

IRDR IRDR

VMT CO CO RCO RCO EffCost CostEffSoc

where all the terms have already been defined.

Applying Technology: As described above, for any indivi dual vehicle, technologies are applied in the order in which they are listed in the Tec hnology file for the relevant vehicle type. Thus, at the beginning of the evaluation for a manufacturer, the only technology which is available to each vehicle is technology package 1 for its vehicle type. Thus, the OMEGA model compares the TARFs for applying technology pack age 1 to all of the vehicles in that manufacturer’s fleet and chooses the vehicle the most negative TARF. The model then applies technology package 1 to that vehicle and reduces its GHG emission level. The formulae for

36calculating the new tailpipe and refrigerant CO 2 em ission lev els after application of the technology are shown below:

If CAP is greater than AIE: 

##  

TEBAIE AIECAP CO CO t t  

 

  1 12 2 1



##  

1 1 1 12 2

    

 



t t t
t GWP GWP
TEBAIE AIECAP RCO RCO

If AEI is equal to or greater than the CAP:

1 22

  t t COCO

1 22

  t t RCO RCO

Here, both the presence of the tech nology in the baseline configur ation of this vehicle and the cap on technology application are considered. The terms shown in the denominators of both equations are included due to the definition of the incremental effectiv eness of technology. When calculating the effectiveness of a technology, the user shoul d assume that the vehicle to which this technology is being a pplied has all of the previous technology packages applied, but none of the additional technologies included in the technology of in terest. Thus, the incremental effectiveness of hybridization over an advanced gasoline engine and transmission package might be 10%. If a specific vehicle or more likely group of vehicles already refl ected half of this 10% hybrid benefit, it would not be correct to simp ly reduce the effectivene ss of the hybrid package to half of the 10% (i.e., 5%) and multiply base em issions by 0.95. It is more accurate to remove the effect of the partial hybr idization by dividing by 100% minus 5% (i.e., 0.95) and then multiplying by100% minus 10% (i.e., 0.9). The net effect in this case is to multiply base emissions by 0.9474, which produces a slightly greater emission reduction than multiplying by 0.95. For modest technologies, the difference is small. However, for technologies which might reduce emissions by 30% or more, the differenc e in methodology becomes more significant.

The model also adds the cost to the manufactur er’s running total cost and recalculates the manufacturer’s corporate average emission leve l. The formulae used to perform these two calculations are as follows:

v t t Sales CEBCAPMIN TechCost TotalCost TotalCost *)0), ((* 1


 




##  


#  





  

 V

v v v V

v t v v

step LifeVMT Sales TCO LifeVMT sSale FleetTCO

1 1 2
2

where v represents a vehicle produced by the manufacturer, V is the total number of vehicles produced by that manufacturer,

37t t t RCO CO TCO 222  , which are the emissions after application of technology t, and LifeVMT v is the lifetime VMT of the class of vehicle to which vehicle v belongs. Lifetime VMT is the sum of the product of releva nt vehicle survival fractions and annual VMT by vehicle age as input in the Reference file.

As mentioned above, compliance with the CO2 standards is performe d on a sales and VMT weighted basis, for ease in trad ing between cars and trucks unde r the provisions of the proposed CO2 standards.

At this point, the model checks to see if th e manufacturer is in compliance with its CO2 emission standard. If so, it stop s and moves on to the next manufact urer. If not, the model then evaluates the TARF for the next technology package for the vehicle which just received the last technology package (technology package 2 in this cas e). The model calculate s the TARF for this package and compares it to the TARFs for th e technology packages 1 for all of the other vehicles. This sequence of adding one tec hnology package to one vehicle, reevaluating compliance, and so on, continues until either the manufacturer complies or until all the available technologies have been applied. When either of these two endpoints is reached, the model moves to evaluating the next manufactu rer or the next redesign cycle.

The model keeps track of a number of inte rmediate values throughout the process of adding technology. These include the order of technology application, the TARFs for each combination of vehicle and technology, the cumulati ve cost per vehicle, et c. for use in producing the various output files.

IV. Output Files

The model provides the output from a run in five different formats. Four of the files are produced immediately at the end of a model run. The name of each of these files is the name of the scenario listed in Column B of the Scenario s tab of the Scenario file. The files differ according to the extension used at the end of the file name. The fifth output file is produced by the user by loading one of the ot her four files. Each of thes e output files and their purpose are described below.

A. Log of Technology Application Steps – Text Format

This file depicts the process of technol ogy application to each manufacturer’s fleet and shows the progress being made towards compliance with the specified standards. The beginning of the file lists basic information about the m odel user, the version of OMEGA being used and the input files. It then pr esents the technology application information by redesign cycle and within each redesign cycle by ma nufacturer and within each manu facturer by vehicle class (i.e., cars first and then trucks). If a combined car-tru ck standard is being eval uated, then only one set of technology application steps is presented which combines bot h types of vehicles. At the beginning of each redesign cycle, the file describe s the standard or standards being evaluated. For universal standards, this means the standard itself. For footprint-ba sed standards, the four

38coefficients described in the inpu t file are listed. The f ile then begins a sequence of sections which apply to a single m anu facturer and single vehicl e class, as shown below.

BMW Baseline Co2avg = 328.74, Target Co2avg = 252.88, Fleet Type = 'C'

Step Index Type Co2pipe Co2tot TP TARF Co2pipe Co2tot Co2avg TotCost 1- 5 6 426.50 426.50 1 -0.1028 388.51 388.51 -> 327.92 4,782,775

The first line lists the manufacturer, it starting CO2 emission level, its CO2 target or standard for this scenario and the vehicle class (C for car or combine, and T for Truck). The starting CO2 emission level is simply the base CO2 emission level of each vehicle weighted by vehicle sales in this redesign cycle. The CO2 sta ndard is either the universal standard listed in the Scenario file, or the foot-pri nt based CO2 standard applied to that manufacturer’s vehicles.

Step is simply a numerical counter followi ng by a hyphen. Index identifies the vehicle receiving the technology or package and refers to the vehicle number in the Market file. Type shows the vehicle type for this vehicle. The first column labeled Co2pipe shows the tailpipe CO2 emissions prior to the appl ication of technology. For the application of technology package 1, this value is the base CO2 emission level show n in the Market file. For subsequent packages, it is the CO2 emission level result ing from the application of th e previous technology package. The first column labeled Co2tot stands for tota l CO2 equivalent emissions and is analogous to Co2pipe. Co2tot equals Co2pipe plus refrigerant emissions in terms of equivalent CO2 emissions. Again, this is either the base refri gerant emission level or the refrigerant emission level after application of the pr evious technology package. TP is the technology package added in this step. TARF is the va lue of the TARF for this packag e applied to this vehicle.

The second column labeled Co2pipe show s the tailpipe CO2 emissions after the application of the technology package. This value will be consider the efficiency of the technology package, as well as the percentage of that package already present on the vehicle (i.e., the TEB). The second column labeled Co2tot shows this value plus the level of refrigerant emissions existing after any control whic h have been applied to these.

Co2avg represents the corporate average CO 2 emission level for this manufacturer and vehicle class. It will genera lly decrease after each step of technology application and show progress towards the manufacturer’s standard. O ccasionally, this value will remain constant. This could be due to round off. Or, the vehi cle receiving the technology may have already been completely equipped with that technology. The OMEGA model still ap plies all applicable technologies to each vehicle when its TARF shows that this is the next best step. If the TEB is 100%, the model simply holds both emissions and co sts constant when it a pplies the technology.

TotCost represents the cumu lative cost in dollars of the technology added up to that point. The increment in this value from step to step is the product of three terms: 1) the cost of the technology package being added, 2) the sales of the vehicle in this redesign cycle, and 3) one minus the percentage of that package alrea dy present in terms of cost (i.e., CEB).

39The very last step of technology add ition fo r each m anufacturer and vehicle class may be indicated by repeating the previ ous step number and adding “OC”. In this case, this step does not represent the addition of technology, but a b ackwards interpolation between the previous two steps which eliminates the over-compliance wh ich usually results from adding discrete technologies to specific vehicles. The presence of this interpolat ion step depends on the value of the threshold cost input specified in the Scenario file and the cost of the last technology package, as discussed above under Input files.

This file can be very useful both for diagnos tic and analytical purpos es. The presence of the TARF values in the file can be useful in identifying technology packages which have relative poor cost effectiveness followed by packages with be tter cost effectiveness. In general, the list of TARF values should start out very negativ e and become more positive with continued technology application. The only exception to th is should be when the same vehicle receives technology in sequential steps. In this case, the TARF may become more negative in a successive step, since the OMEGA model could not consider the more cost effective technology until it applied the previous technology to that vehicle. When the user observes a series of technologies being applied to the same vehicl e in successive modeling steps, the model is indicating that it would prefer to combine two or more of the technology packages and that separating them is providing little purpose in the model run. This is an indication that the order of technologies might not be appr opriate (or there might be an error in one of the technology input values).

From an analytical perspective, this file presents a series of cumulative costs as each manufacturer’s average CO2 level is reduced. If a sufficiently stringent CO2 standard (e.g., 50 g/mi) is input to the model, manufacturers will not comply with the specified standard. However, the model will apply all of the technolo gy available to each vehicle. Thus, the file represents a complete relationship between co st and emissions for each manufacturer. By reading the information contained in this file into a spreadsheet or other program capable of analysis, the user can quickly determine the co st for a wide range of CO2 standards without rerunning OMEGA each time. Since the social costs and benefits of CO2 standards tend to be linear with CO2 level, it is possible to repres ent these costs and benefits with simple linear equations. The user can then easily combine the relationship between manufacturer’s emission levels and technology costs with so cial costs and benefits at an industry-wide leve l. It would then be possible to solve for the industry-wid e emission levels which maximized net societal benefits, produced zero net societal benef it or met some other economic criteria.

Example : The following table depicts a portion of the log file from an OMEGA model run using the sample input files.

CYCLE 1 Target Coeffs: (A=204.0, B=275.0, C=41.0, D=56.0) , (A=246.0, B=347.0, C=41.0, D=66.0) Industry Baseline Co2avg = 287.35, Targ et Co2avg = 223.31, Fleet Type = 'C' Step Index Type Co2pipe Co2tot TP TARF Co2pipe Co2tot Co2avg TotCost

1- 19 6 405.13 405.13 1 -0.1266 375.33 375.33 286.92 33,256,5 14 6 403.17 403.17 1 -0.1264 372.63 372.63 286.61 57,241, 9 2- 3- 29 13 446.63 446.63 1 -0.1261 412.62 412.62 286.61 57,306,3 13 10 392.46 392.46 1 -0.1255 362.57 362.57 286.44 70,885, 0 4-

405- 18 10 389.46 389.46 1 -0.1253 359.8 359.8 286.28 83,589, 9

33- 28 2 232.56 232.56 1 -0.1106 217.03 217.03 267.17 2,117,945, 6 34- 14 6 372.63 372.63 2 -0.0904 357.54 357.54 267.01 2,158,753,1 35- 14 6 357.54 357.54 3 -0.0973 305.57 305.57 266.49 2,238,406,5 36- 19 6 375.33 375.33 2 -0.0901 355.62 355.62 266.21 2,297,680,8

54- 7 5 325.61 325.61 2 -0.0615 300.75 300.75 244.96 6,904,283, 7 55- 7 5 300.75 300.75 3 -0.0779 250.06 250.06 243.95 7,106,439,8

93- 28 2 217.03 217.03 2 -0.0426 203.45 203.45 221.06 12,799,723, 9 93-OC 28 2 217.03 217.03 2 -0.0426 216.53 216.53 223.31 11,973,039,1

The first five technologies added to cars in this model run were all the first technology listed for each vehicle type (TP = 1). The same is true fo r steps 6-32 (not shown). Step 33 also adds the first technology available to vehicle 28. Step 34 adds the second technology available to vehicle 14, while step 35 adds the third technology availabl e to this vehicle. This sequence demonstrates how the model evaluates the next available techno logy for each vehicle when deciding where to add technology next. In this ca se, technology number 3 for vehicle 14 was preferred over any of the second technologies for all th e other vehicles. Examination of the TARF values explains why this was the case.

The TARF for the technology number 2 for vehicle 14 was-0.0904, while that for technology 3 was -0.0973. Thus, if -0.0904 was th e lowest TARF for step 34, an even lower TARF would have to be the lowest TARF in step 35. When it is observed that the model adds two technologies in a row to the same vehicle, the user should question whether the first technology should be modified or the two technologies should be combined. The reason for this is that, unless step 34 is the very last technol ogy added to enable compliance, the model will never simply add technology 2 to vehicle 14. Ve hicle 14 will always re ceive either technology 1 or 3. The primary concern is that a relati vely high TARF value for technology 2 could inappropriately inhibit the appli cation of technologies 2 and 3 to this vehicle relative to the application of technologie s to other vehicles.

In this case, the TARFs for technologies 2 a nd 3 are quite close. Thus, combining them into one technology package would not produce s ubstantively different results. The same sequence occurs with vehicle 7 later in the run (steps 54 and 55 in the above table). In this latter case, the difference between the TARFs is much greater. Combining the two steps may have resulted in the earlier application of the two t echnologies than applyi ng them separately.

The final two lines of the above table illust rate the interpolation that occurs in the compliance determination if the cost of the la st technology added exceeds the “threshold” input in the Scenario file. After step 93, the manu facturer’s corporate av erage CO2 level was 221.06 g/mi, while the standard was 223.31 g/mi. The model adjusted the manufacturer’s corporate average CO2 level to 223.31 g/mi by only appl ying technology 2 to a portion of the sales of vehicle 28. This reduced the overall cost of compliance from $12.8 billion to $12.0 billion.

41B. Log of Technology Application Ranking Factors – Text Format

This file presents a list of all the technology packages ava ilable to each vehicle and the TARF calculated for each combination. The name of this file has th e acronym TARF appended to it. The acronyms used in this file are the same as those described above. This file presents a useful summary of all the TARF values to aid th e user to evaluate the appropriateness of the technology packages made availa ble to each vehicle type.

The current file format presents the TARF values first by technology package, then by vehicle index. This provides a useful comp arison of each technology package across a range of vehicles. The user may also want to read this file into a spreadsheet and sort by vehicle index and then technology package.

C. Summary of Cost and Emissions – Excel ® Format

This file presents a summary of vehicl e and manufacturer costs and emission levels resulting from a model run. Th e file starts with summary info rmation about the run, the model user, the version of OMEGA being used and the valu es input on the first tab of the Scenario file. The file then lists four types of information by manufacturer and vehicle cl ass: 1) vehicle sales, total costs, cost per vehicle and emission level per vehicle.

The file continues by presenting the cost of the technology added to each vehicle and the last technology package added to each vehicle. The last section of the file presents the series of technologies added to each vehicle. For each technology, the file shows the step (for each manufacturer-vehicle class co mbination) in which the technology was added, the vehicle’s emission levels before and after this technology and the incremental total cost of adding this technology.

D. Summary of Cost and Emissions – XML Format

This file contains an extensive amount of data from both the input files and the other output files. It is currently us ed in conjunction with the benefits calculation spreadsheet. The benefit calculation spreadsheet loads the information contained in the XML file and produces estimates of the societal costs and benefits a ssociated with the CO2 emission control. The information in the XML file can be loaded into a spreadsheet or databa se program. However, there are too many columns in the file for it to completely load into many spreadsheets. Therefore, use of a database program would be pr eferable. As the primary use of this file is associated with the benefits calculation spreadsheet, discussion of the information contained in this file will be discussed under th e benefit calculation spreadsheet.

42Appendix 6 contains a com pilation of variab le de f initions used in this section.

Car-Truck Trading Algorithm

The Clean Air Act allows trading between a manufacturer’s car and truck fleets to the fullest extent. Thus, EPA has designed the model such that the user can utilize a full range of trading scenarios. This section describes the ad ditional logic required when the user enters in a non-zero value into column I of the Scenario input file, triggering a trading scenario.

1) Like the non-trading scenario, the first step of this algorithm is to determine the overall sales-weighted emissi ons target for the manufactur er with cars and trucks combined:

trucks cars trucks trucks cars cars Sales Sales Standard Sales Standard Sales Target.mfr    



2) Then, the model determines the starti ng point for each manufacturer using the same basic equation

trucks cars trucks trucks cars cars Sales Sales ns aseEmissio B Sales ns aseEmissio BSales Target.mfr    



3) Because the lifetime VMT of a truck is gene rally higher than that of a car and in most cases will emit more GHG over its lif etime by virthe, EPA has chosen to account for lifetime VMT when trading be tween a manufacturer ’s car and truck fleet. In this step, the model determines VMT-weighted emissions targets for the manufacturers.

E. Benefits Calculation Spreadsheet – XLS Format

The benefits calculation spr eadsheet provides a useful way to automatically extend the core analysis of the application of technology to reduce CO2 emissions to the estimation of national costs and benefits. The model estimates discounted costs and societal benefits on a calendar year basis. Except for technology cost s, all costs and benefits are estimated on a fleetwide basis. Industry fleetwi de technology costs are estimated, but they are also estimated on a disaggregated basi s by manufacturer.

Unlike the other four output files, the benefits calculation spreadsheet is not automatically produced by the OMEGA model after completion of a model run. The user must open the default benefits calculati on file (or a revised version cr eated by the user), which is a Microsoft© Excel®© file. The defa ult file opens to the load work sheet. The user clicks on the “Load” button and a window opens which shows the XML format files available for loading in the directory or subdirectory of the benefits ca lculation spreadsheet. The user double clicks on

43the appropriate XML file from the OMEGA mo del r un to be analyzed in the benefits calculation spreadshe et and the relevant information from the XML file loads into the spreadsheet and produces estimates of various costs and societal benefits associated with CO2 emission control, as described further below.

The benefits calculation spreadsheet should be considered a framework for the estimation of costs and benefits associated with CO2 emission control from vehicles. As described below, the actual costs and benefits estimated by th e spreadsheet are highly dependent on a large number of inputs. EPA provides a set of inputs in the default sp readsheet distributed with the OMEGA model. References for th ese inputs are provided below. Ho wever, in general, the user is responsible that the specific inputs used in any analysis are consistent with the specific analysis being performed. These may be those contained in the default spreadsheet or others chosen by the user. The spreadsheet format make s substitution of alternative input assumptions and estimates easy to perform. However, as in a ll spreadsheets, the user must be careful to be aware of all the consequences of specific changes to input cells. The Tools/Formula Auditing/Trace Dependents option can be very useful in this regard.

The user should note that the version of the benefits calcul ation spreadsheet contains one error which is described below. This error will be corrected in a revised version of the benefits calculation spreadsheet which will be posted in the near future on the OMEGA website (www.epg.gov/otaq/climate/models.htm). The r eader should go to this website and download the installation file, which contains the benefits calculation spreadsheet. The user should also note that the last worksheet contained in the sp readsheet, labeled Benefi ts Summary, contains draft information for future versi ons of the spreadsheet and does not currently provide any useful information.

Overview of the Calculation of Costs and Societal Benefits

The spreadsheet can be broken down into 11 major steps:

1) Reading in of the model’s estimate of 1) CO2 emissions per vehicle for each redesign cycle, 2) annual technology costs by manufacturer for each redesign cycle, and 3) other relevant factors from the various input files, such as annual VMT and vehicle sc rappage rates by vehicle age, fuel prices and physical a nd chemical properties, the disc ount rate, the gap between onroad and certification fuel economy, etc.

2) Calculation of industry-wide CO2 emissions per mile and average fuel consumption per mile, both by vehicle class and redesign cycle.

3) Calculation of technology costs (per vehicle and annual) by manufacturer, and redesign cycle.

4) Interpolation of vehicle sales, CO2 emi ssion levels, fuel consum ption, and technology cost per vehicle for model years between redesign cycles.

445) Estimation of the impact of the rebou nd effect on per vehicle VMT per year for each model year.

6) Calculation of fleet-wide VMT, fleet -wide CO2 emissions and fleet-wide fuel consumption by vehicle class by calendar year.

8) Calculation of the change in upstream emi ssions from the change in fleet-wide fuel consumption by calendar year.

9) Calculation of the change in downstream (v ehicular) emissions, from the change in fleetwide VMT by calendar year.

10) Calculation of technology co sts, fuel savings, and the soci etal value of the changes in emissions, driving, noise, congestion, accidents, re fueling time and crude oil related externalities by calendar year.

11) Adjustment of fleet-wide VMT per calendar year to be consistent with an external source.

Loading data: The procedure used to read in or lo ad model input and output values into the benefits calculation spreadshee t are described below in Section III., Model Operation. At the most fundamental level, the model outputs which ar e read in include the list of vehicles which have been modeled, their manuf acturer, vehicle class, sales, and starting and finishing CO2 emission levels. This information appears in the worksheet Model Resu lts. Technology costs are read into the Tech Costs worksheet in terms of annual costs by manufacturer for each redesign cycle. Other relevant da ta from the Fuels, Reference and Scenario files are loaded into the Shared Inputs worksheet.

Industry-wide CO2 emissions per mile a nd average fuel consumption per mile: These values are calculated for each manufacturer , vehicle class and redesign cycle by weighting the cost per vehicle by each vehicle’s sales. These calculations are performed in the Model Results worksheet. Currently, the benefits calcul ation spreadsheet assumes that all vehicles are gasoline-fueled when converting CO2 emissions to fuel consumption. The user would have to adjust the fuel consumption for any diesel ve hicles being modeled if their numbers were significant.

Technology costs per vehicle by manufacturer : These values are calculated in the Tech Cost worksheet by dividing annual costs by manufacturer by sales per manufacturer. Total annual costs for each redesign cycle are calc ulated by summing annual costs across all manufacturers.

Model years between redesign cycles: Four factors are estimated for each calendar based on values for each redesign cycle. Tota l annual technology costs ar e calculated by linearly interpolating the fleet-wide annual cost for each redesign cycle. (Technology costs are assumed to be zero in the baseline year.) For simplicity, we assume that all of the sales of a given model year’s vehicles occurs in the calendar year of the same value (i.e., model year equals calendar year). This calculation is performe d in the Tech Cost worksheet.

45Annual vehicle sales by vehicle class are calculated similarly, except that baseline sales are those co ntained in th e Market file and read into the Model Results worksheet. For example, if the baseline year is 2010, re design year 1 is 2015 and redesi gn year 2 is 2020, the spreadsheet linearly interpolates between the 2010 and 2015 sales to estimate annual sales for years 2011- 2014. Likewise, the spreadsh eet linearly interpolates be tween the 2015 and 2020 sales to estimate annual sales for years 2016-2019.

CO2 emissions by vehicle class are calculated for each model year in columns P and R of the VMT_Rebound_Effect worksheet. (The estimate s of per vehicle emissions are embedded in the formulae which calculate total CO2 emissions fr om all vehicles of a specific model year and age.) Fuel consumption per mile by vehicle clas s are calculated for each model year in columns AE and AG of the VMT_Rebound_Effect worksheet. (The estimates of per vehicle fuel consumption are embedded in the formulae which calculate total fuel consumption from all vehicles of a specific model year and age.)

Increase in VMT due to r ebound effect: The rebound effect is de fined as the ratio of the percentage change in VMT to the percentage change in incremental cost of driving, which is typically assumed to simply be the incremental co st of fuel consumed per mile. As mentioned above, the economic concept is that as drivi ng becomes cheaper, people tend to drive more. Since VMT increases with a reduction in fuel consumption, the sign of the rebound effect is negative. The rebound effect (REB) is an input on the “Economics ” worksheet of the “Scenario.xls” file. The percentage increase in VMT for a given change in fuel consumption per mile is calculated as follows: 

##  

old old new reb FleetFC FleetFC FleetFC REB VMT   *

Where REB is the rebound effect, FC is fuel consumption per mile, new is the value after CO2 emission control and old is the baseline value and VMT  is the percentage change in VMT per vehicle.

Since fuel consumption changes by model year , each model year’s vehicles will reflect a distinct change in VMT. This change in VMT is assumed to continue throughout the life of the vehicle, since fuel economy is assumed to be constant throughout vehicl e life. Also, only new vehicles are affected by the additional technology . Thus, at any point in time, the VMT by some vehicles will be affected by the rebound effect and that by other vehicles will not be affected.

The rebound effect applicable to each model year and vehicle class is calculated in the VMT_Lookup worksheet. This effect is applied to the base estimates of annual VMT per vehicle by age (contained in the Reference file and read into the benefits calculation worksheet on the Shared Inputs worksheet) to generate annua l VMT per vehicle estimates which include the rebound effect.

Fleet-wide VMT, CO2 emissions and fu el consumption by calendar year: These parameters are calculated on the Emissions_Fue l_Consv worksheet. Fleet-wide VMT is the sum

46of VMT from vehicles sold in th e baseline model year and earlier and vehi cles sold in the firs t year of the first redesign cycle and later. Th e first set of vehicles is unaffected by the CO2 emission control being modeled. Parameters relate d to this set of vehicles are referred to as Baseline and Earlier in the relevant column head ings. Their VMT is a simple function of inputs to the model and are calculated in Column AF of the Exclusive Inputs worksheet. The VMT for each vehicle class in each calendar year is a function of sales in each model year starting with that calendar year and earlier, the VMT per vehicle by age and the percentage of vehicle sales surviving by age. Historic sales are show n in Columns Y and Z of the Exclusive Inputs worksheet. These values are only used in the Benefits Calculation spreadsheet and not in the actual running of the OMEGA model. Vehicle sales starting with the baseli ne year specified in the model run override the historic values in case of overlap. The estimates of historic vehicle sales contained in the default spreadsheet we re taken from EPA’s Motor Vehicle Emission Simulator (MOVES).

The second set of vehicles includes those affected by CO2 contro l (i.e., their VMT is affected by the rebound effect). Again, the VMT for each vehicle class in a given calendar year is a function of sales in each model year between that calendar year and the first year of the first redesign cycle, the VMT per vehicle by age adjust ed for rebound and the percentage of vehicle sales surviving by age. Because of the rebound e ffect, this calculation is much more complex than that for historic vehicles. The VM T_Rebound_Effect worksheet calculates total annual VMT from each model year’s vehicles in each re levant calendar year. This calculation is performed for these vehicles both before and afte r CO2 emission control, so that the benefit of control can be determined. The sum of the VMT from historic vehicles and controlled vehicles is performed on the Emissions_Fuel_Consv worksheet.

The calculation of fleet-wide CO2 emissions and fuel consumption by calendar year is performed analogously to that of VMT. For hi storic vehicles, the level of CO2 emissions by model year is added to the combination of ve hicle sales, VMT per vehicle, and survival percentages. This calculation is performed in Columns AB, AC and AD of the Exclusive Inputs worksheet. The estimates of CO2 emissions from historic vehicle contained in the default spreadsheet were taken from MOVES.

With respect to controlled vehicles, total CO2 emissions from each model year’s vehicles in each relevant calendar year are calculated on the VMT_ Rebound_Effect worksheet. This involves simply multiplying the VMT for that vehi cle cohort by its average CO2 emission level. This is done for these vehicles both before and after control. Fuel c onsumption is currently estimated from CO2 emissions assuming that all fuel consumed is gasoline.

Upstream emissions: C hanges in upstream emissions occu r due to the reduction in fuel production and distribution resulting from less vehicle fuel consumption. The benefits calculation worksheet currently tracks six upstr eam pollutants: CO2, CO, VOC, NOx, SOx, and PM. The model only calculates the change in thes e upstream emissions, not their absolute values before and after control. The change in each pollutant’s emissions in each calendar year is simply the product of the total reduction in fu el consumption (after accounting for the rebound effect), conversion of this fuel volume to energy content (Btu), the estimate of the reduction in upstream emissions in grams per Btu of fuel pr oduced and distributed per reduction in fuel

47production, and conversion to metr ic tons of emis sions. The change in fuel consum ption is simply the difference in fuel consumption from the application of CO2 control as described in the previous section. The other factors are si mply inputs on the Exclus ive_Inputs worksheet and are only used in the Benefits Calculation spreadsheet. In general, the values for the levels of upstream emissions per Btu of fuel produced shoul d apply to the expected change in upstream emissions for an incremental change in fuel pr oduction from otherwise ex pected levels. These values may be different from the average upstrea m emissions per unit of fuel production. Use of the incremental emissions is most appropriate, since the Benefits Calculation spreadsheet only calculates the change in these emissions and not total emissions before and after vehicle CO2 control. The change in upstream emissions are calculated in Columns S through W of the Emissions_Fuel_Consv worksheet. Positive va lues indicate reductions in emissions.

Downstream e missions: C hanges in downstream emissions (i.e., emissions from vehicles other than CO2) occur due to increase d VMT related to the rebo und effect. Other than CO2, emissions from vehicles per mile are as sumed currently to be unaffected by CO2 emission controls. Emissions from vehicles produced prior to control are not affected. As with upstream emissions, the benefits calculation worksheet cu rrently tracks six upstream pollutants: CO, VOC, NOx, PM, and Sox and only changes in these emissions are calculated.

Emissions of these pollutants from vehicles usually vary with vehi cle age. The g/mi emission levels of each pollutant are input to th e model on the Exclusive_Inputs worksheet in the form of linear or quadratic equations as a functio n of vehicle age. These equations were derived from emission factors taken from EPA’s MOBILE6 model and used in it EPA’s ANPRM for the control of vehicle GHG emissions. 4 Vehicles of all model years are assumed to have the same emissions. This is reasonable since emissions from only CO2 controlle d vehicles are being evaluated. Thus, currently, the emission levels in the Benefits Calculation spreadsheet assume that all vehicles were designed to comply with the EPA Tier 2 emission standards. The change in the total emissions of each pol lutant from vehicles of a specific model year and age are calculated on the VMT_Rebound_Effect worksheet ba sed on the increase in VMT of that model year’s vehicles in a particular calendar year mu ltiplied by the emission factor of that pollutant for vehicles of that age. Negative values indicate increases in emissions.

Fuel savings and societal benefits : The value of reduced fuel consumption is calculated on the Externalities worksheet. The value of redu ced fuel consumption in each calendar year is simply the product of the reduction in fuel consumpti on and the price of fuel. The price of fuel is specified in the Fuels file and input to the Benefits Calculatio n spreadsheet on the Shared_Inputs worksheet. The fuel prices used by the core model to rank and apply technology should include excise taxes, since this is the fuel price perceived by consumers. However, since the benefits calculation spreadsheet focuses on societal bene fits, excise taxes should be removed when calculating the value of reduced fuel consump tion. Currently, the benefits calculation spreadsheet uses the fuel prices directly from the Fuels file, so taxes are included. This will be corrected in the future.

4 Vehicle Technical Support Docu ment: Evaluating Potential GHG Reduction Programs for Light Vehicles, U.S. EPA, June 16, 2008, EPA-HQ-OAR-2008-0318-0084., http://www.regulations .gov/search/Regs/home.html#doc umentDetail?R= 09000064806 692a5

48The Benefits Calculatio n spreadshe et allows for the estimation of additional values related to reductions in crude oil based fuel consumption. These externalities vary from study to study, but can include monopsony effects, oil security effects, price shock eff ect, etc. Values for such externalities can be input on the Exclusive inputs wo rksheet in terms of dollars per gallon of fuel consumed. The total societal benefit related to the reductions in these externalities is simply the product of the total re duction in fuel consumed in each ca lendar year and the total value of the externalities. This calculation is performed on the Externalities worksheet. The current total value of these externalities contained in the de fault spreadsheet is $0.285 per gallon. EPA used an estimate of $0.34 per gallon in its ANPRM fo r the control of vehicle GHG emissions. EPA used an estimate of $0.467 per gallon in its recent NPRM for the control of vehicle GHG emissions.

The Benefits Calculation spreadsheet also allows for the estimation of several driving related externalities, namely noise, congestion, accidents, refueling time and the value of additional driving itself. All of these, except for refueling time, are related to the additional driving associated with the rebound effect. Their value in each cas e is simply the product of the increase in VMT in a calendar year times the value of that externality per mile. The values of the externalities related to noise, congestion, and accidents are input on the Exclusive_Inputs worksheet and are those used in EPA’s recent NPRM for the control of vehicle GHG emissions.

The value of the reduction in refueling time assumes that manufacturers maintain fuel tank size with CO2 control and that people typica lly refuel when their tank reaches a certain level. The reduction in the number of refuelings in a specific calendar year is a function of the average fuel tank size of the vehicle (in gall ons), an average amount of fuel added during refueling (in percent of tank volume), and the reduc tion in total fuel consumption in that calendar year. The value of the time saved in based on an average amount of time spent refueling a vehicle (in minutes), the value of time for the driver (in doll ars per hour) and other vehicle occupants and the number of total occupants in the vehicle when re fueling occurs. All of these values, except for the total re duction in fuel consumption ar e input on the Exclusive_Inputs worksheet. These values are consistent with those used in EPA’s ANPRM for the control of vehicle GHG emissions referenced above. The cu rrent Benefits Calculation does not consider the time to recharge batteries for plug-in or battery electric vehicles.

The value of increased driving associated with the rebound effect is the sum of two components. The first component is the incrementa l cost of driving per mi le. This is based on the assumption that the value of in creased travel must exceed its dire ct cost or the driver will not take the additional trip. The second component is the consumer surplus generated by this driving.

The direct incremental driving is assumed to be cost of fuel consumed plus that of the additional congestion caused. The additional noise and accidents are not considered to be direct costs. Noise is primarily experienced by non-driv ers and therefore not considered by drivers in their decision to take an addi tional trip. While accidents tend to be proportional to mileage, especially for a given driver, we assume that most of the cost of accident s is borne by insurance, where there is only a weak association with mil eage driven. Congestion, on the other hand, is totally experienced by drivers and experienced fully each trip. Ho wever, it should be noted that

49EPA did not include the incremental cost of c ongestion in its recent NRPM for the control of GHG e m issions from vehicles. There, we assume d that most of the congestion experienced by additional driving was experienced by other driver s, so they would not be considered by the driver contemplating an additional trip.

The change in the consumer surplus is esti mated to be one half of the change in VMT times the change in the cost of driving, which here is the reduction in fuel cost per mile. All of these terms have already been estimated for ot her purposes within the Benefits Calculation spreadsheet. The reader should note that the version of the Be nefits Calculation spreadsheet included with the model installation package incorre ctly calculates the cost of fuel needed to drive the additional VMT. This will be corrected in a revised version of the benefits calculation spreadsheet which will be posted in the near future on EPA’s OMEGA website at (www.epg.gov/otaq/climate/models.htm).

The value of the changes in CO2 and other pollutant emissions are simply the product of the changes in these emissions by calendar times their value in dollars per metric ton. The societal benefits associated with upstream emi ssion changes are calculated on the UpstreamCosts ($) worksheet, while those for downstream emission changes are calculated on the DownstreamCosts ($) worksheet. The societal values for the reduction in CO, VOC, NOx, PM, and SOx emissions are input in cells A15:B23 of the Exclusive_ Inputs worksheet. These values are assumed to be constant in real dollars ove r the timeframe of the analysis. The values contained in the default spreadsheet are consis tent with those used in EPA’s ANPRM for the control of vehicle GHG emissions referenced above.

The value of CO2 emission reductions is inpu t to the model through the Scenario file. This value is specified in terms of real dollars and can vary over time (e.g., increase at a fixed percentage rate per year in real dollars relative to a specified value in a specified base year). The societal benefits associated with upstream CO2 emission changes are calculated on the UpstreamCosts ($) worksheet, while those for do wnstream CO2 emission changes are calculated on the DownstreamCosts ($) worksheet. The value for CO2 emission reductions is $7.50 per ton in 2007 dollars and increases at 2.4% per ye ar. EPA’s recent NPRM for controlling GHG emissions from light vehicles evaluates a rang e of values for CO2 emission reductions from $5 per metric ton to $56 per metric ton. Given this wide range of values, th e user should consider it her or his responsibility to just ify the use of a specific value for CO2 emission control in any specific analysis using this modeling tool.

Summary of costs and societal benefits: The societal costs and benefits other than technology costs are summarized on the Non-Tech Costs worksheet. The convention assumes that costs are positive and benef its are negative. As mentioned above, total technology costs per year are calculated on the Tech Costs worksheet. These two sets of values are both shown on the All Costs worksheet, again with costs being positive and benefits being negative.

Normalization to an external source of fleet-wide VMT per year: The spreadsheet allows the user to adjust the total VMT estimated for the fleet in each calendar year to that from an external source. The calculation of fleet-w ide VMT described above can be described as a “bottom up” estimate. Vehicle sales are estimated for each model year, vehicles are scrapped at

50a specified rate, and vehicles of a specified age drive a specified number of m iles over the course of a year. Such bottom -up estimates usually do not match top-down estimates of total VMT which are produced from measurements of actual dr iving (e.g., from pressure or magnetic strips placed across roadways or from other traffic counts). The default benefits calculation spreadsheet includes estimates of fleet-wide VMT per calendar year from the Published 2009 Annual Energy Outlook produced by the Energy Information Administration. 5

These fleet-wide VMT estimates are input in Column O of the AEO Adjustment Calculations worksheet. The ratio of these VMT estimates to those of the bottom up process for each calendar year is calculated in Column N. The estimates of all costs and benefits presented on the All Cost worksheet are multiplied by these ratios, except for the technology costs, which are just a function of vehicle sales and not VMT. The results are shown on the VMTAdjCosts worksheet.

Running the Model

Model Operation

Installing the model should add an “OMEGA” icon to the user’s desktop. Omega is started by double clicking on this icon. A graphi c user interface (GUI) should appear in a few seconds. The current GUI is very simple and relies on the fact that all of the information needed to run the model is contained in the input files. (Future ve rsions of the OMEGA model may provide the user with the ability to modify the inputs through th e interface.) The user should then click on “File” in the menu bar and choos e “Open” from the drop down menu. A window will open which displays the files contained in the model’s input directory. The user should double click on the desired Scenario file and the model will loads the data contained in this file and that in the Market, Technology, Fuel and Reference files specifi ed in the Scenario file. The GUI will update and display portions of the Scenario, Market and Technology input files. The current GUI allows the user to partially navigate through these three files in order to confirm that the correct input files were specified in the Scen ario file. When everything is loaded, the car icon will turn green. The user can now run the ca ses included in the Scenario file by clicking on the green car button.

As the model is running, the lower left hand corner of the GUI will indicate progress (e.g., processing Scenario 1 of 5, processing Scen ario 2 of 5, etc.). The model normally takes less than a minute to run through a fairly extensive scenario. If the Scenario file only specifies one case to be run, when the model is done with this case, a text file showing the sequence of technology addition by manuf acturer and by redesign cycle appears. This file is automatically named with the case name from the Scenario file. If more than one case is run, the indicator in the lower left hand corner will simply say “Done.”

There is an additional saved output file entitled “Tarf = “date-time”, which includes the results of the TARF calculations for each vehicle type-t echnology package combination for each redesign cycle. Go back to the file menu in the GUI, and click on “Save,” which is necessary to enable

5 Annual Energy Outlook 2009 with Projections to 2030, Energy Information Administration, U.S. Dept. of Energy, DOE/EIA-0383(2009), March 2009.

5152 the “visualization” function. In the file menu, now click on “visualization” to examine the results of the technology application process for each vehicle model in the market data file. In the near future, the information in the visualiz ation will be generated directly in an output spreadsheet, but at present it is only available in the visualization, and if desired, the user can copy and paste the results manually. Economic costs and benefits can be loaded in a spreadsheet file entitled “Benefits Calcul ations,” located in the “Output ” folder. Open the “Benefits Calculations” Excel® file, go to the “Load” worksh eet (if the spreadsheet did not open to this worksheet), and press the “Load” bu tton. A list of XML files availa ble to load will appear. The user should double click on the XML file from the m odel run of interest. This loads the results of model run and updates the cost and benefit estimates.