***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 1 

## Chapter 10: **CAF E**  **Model for**  **HD Pickups and Va**  **ns** For this rule, the agencies conducted coordinated and complementary analy ses using two anal yt ical methods for the heavy- duty pick up and van segment by employing both DOT’s CAFE model and EPA’s MOVES model. For heav y- duty pickups and vans, the agencies performed complementary

anal ys es , which we refer to as “Method A” and “Method B ”. In Method A, the CAFE model was used to project a pathway the industry

could use to comply

with each regulatory

alternative and the estimated effects on fuel consumption, emissions, benefits and costs. In Method B, the CAFE model was used to project a pathway the industry

could use to comply with each regulatory

alternative, along with resultant impacts on per vehicle costs, and the MOVES model was used to calculate corresponding changes in total fuel consumption and annual emissions. Additional calculations were performed to determine corresponding monetized program costs and benefits. NHTSA considered Method A as its central anal ys is and Method B as a supplemental anal ys is. EPA considered the results of both methods. The agencies concluded that bo th methods led the agencies to the same conclusions and the same selection of the proposed standards. See Section VII of the preamble for additional discussion of these two methods. In this c hapter, the CAFE modeling s ys tem is described and used to anal y ze technology use and per - vehicle costs under each regulatory alternative, including the no action alternative (which reflects continuation of previousl y- promulgated standards). However, this model is more comprehensive and also projects other impacts. NH TSA addresses these other impacts in the Draft EIS and these are also presented here. 1 

### 10.1 **HD Pick** **up and Van Fleet**  **10.1.1 Why**  **d id the Agencies Develop the Analysis Fleet**  **?** The modeling s ys tem relies on many inputs, including an anal ys is fleet. In order to estimate the impacts of potential standards, it is necessary to estimate the composition of the future vehicle fleet. Doing so enables estimation of the extent to which each manufacturer may need to add technology

in response to a given series of attribute - based s tandards, accounting for the mix and fuel consumption of vehicles in each manufacturer’s regulated fleet. The agencies create an anal ys is fleet in order to track the volumes and ty pes of fuel economy- improving and CO 2 - reducing technologies that are already present in the existing vehicle fleet. This aspect of the anal ys is fleet helps to keep the CAFE model from adding technologies to vehicles that already have these technologies, which would result in “double counting” of technologies’ costs and bene fits. An additional step involved projecting the fleet sales into MYs 2019 - 2030. This represents the fleet volumes that the agencies believe would exist in MYs 2019 - 2030. The following presents an overview of the information and methods applied to devel op the analy sis fleet, and some basic characteristics of that fleet. Details appear in the input file.

1 EPA uses its MOVES model to project these other impacts as discussed in Chapters 5 through 8 of this draft RIA. Note that the results of both modeling approaches corroborate the results of the overall analysis.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 2 

### 10.1.2 **How the MY2014 Based Analysis Fleet Was Developed**  **?** Most of the information about the vehicles that make up the 2014 anal ys is fleet was gathered from the 2014 Pre - Model Year Reports submitted to EPA by the manufacturers under Phase 1 of Fuel Efficiency and GHG Emission Program for Medium - and Heav y- Duty Trucks, MYs 2014 - 2018. The major manufactures of class 2b and class 3 trucks (Chry sler, Ford and GM) wer e asked to voluntarily

submit updates to their Pre - Model Year Reports. Updated data were provided b y Chrys ler and GM. These updated data were used in constructing the anal ys is fleet for these manufacturers. The agencies agreed to treat this information a s Confidential Business Information (CBI) until the publication of the NPRM. This information can be made public at this time because b y now all MY2014 vehicle models have been produced, which makes data about them essentially public information. These data (by

individual vehicle configuration produced in MY2014) include: Projected Production Volume/MY2014 Sales, Drive Ty pe, Axle Ratio, Work Factor, Curb Weight2 , Test Weight 3 , GVWR, GCWR, Fuel Consumption (gal/100 mile) 4 , engine ty pe (gasoline or diesel), engine displacement, transmission ty pe and number of gears 5

. The column “Engine” of the Pre - Model Year report for each OEM was copied to the column “Engine Code” of the vehicle sheet of the CAFE model market data input file. 6 Values of “Engine” were chang ed to Engine Codes for use in the CAFE model. The codes indicated on the vehicle sheet map the detailed engine data on the engine sheet to the appropriate vehicle on the vehicle sheet of the CAFE model input file. The column “Trans Class” of the Pre - Model Year report for each OEM was copied to the column “Transmission Code” of the vehicle sheet of the market data input file. Values of “Trans Class” were changed to Transmission Codes for use in the CAFE model. The codes indicated on the vehicle sheet map the detailed transmission data on the transmission sheet to the appropriate vehicle on the vehicle sheet of the CAFE model input file.

2 GM did not provide curb we ight in its submittal. GM did provide “Payload.” Curb weight for GM vehicles was calculated as GVWR

– Payload. 3 Chrysler and GM did not provide test weights in their submittals. Test weights were calculated as the average of GVWR and curb weight round ed up to the nearest 100 lbs. 4 These values were converted to mile/gal for use in the Volpe model. In their supplemental data submission GM provided the data as mpg in its report column “Fuel Consumption Performance”. In its supplemental data submissio n Fiat provided “Fuel Economy on Primary Fuel (Unadjusted combined CO 2 g/mi) . ” These values were converted to mpg using the factors 8,887 gCO 2/gal for gas engines and 10.180 gCO 2/gal for diesel engines. 5 GM did not provide transmission data in its submitt al. Specific transmissions associated with each of GM’s trucks were identified using information from GM’s websites. 6 The GM data was an exception. In its case the column “Disp” of the Pre - Model Year report was copied to the column “Engine Code.”***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 3 In addition to information about each vehicle, the agencies need additional information about the fuel economy- improving/CO 2 - reducing technologies already on those vehicles in order to assess how much and which technologies to apply to determine a path toward future compliance. Thus, the agencies augmented this information with publicly- available data that includes more complete technology descriptions. Specific engines and transmissions associated with each manufacturer’s trucks were identified using their respective internet sites. Detailed technical data on individual engines and transmissions indicated on the engine she et and transmission sheet of the CAFE model input file were then obtained from manufacturer internet sites, spec sheets and product literature, Ward’s Automotive Group and other commercial internet sites such as cars.com, edmunds.com, and motortrend.com. 7 “Fuel Economy

on Secondary

Fuel” was calculated as E85 = .74 gasoline fuel economy, or B20 = .98 diesel fuel economy . These values were duplicated in the columns “Fuel Econom y (Ethanol - 85)” and “Fuel Economy (Biodiesel - 20)” of the CAFE market data input f ile. Values in the columns “Fuel Share (Gasoline)”, “Fuel Share (Ethanol - 85)”, “Fuel Share (Diesel),” and “Fuel Share (Biodiesel - 20)” are Volpe assumptions. The CAFE model also requires that values of Origin, Regulatory Class, Technology Class, Safety

C lass, and Seating (Max) be present in the file in order for the model to run. Placeholder values were added in these columns. In addition to the data taken from the OEM Pre Model Year submittals, NHTSA added additional data for use b y the CAFE model. The se included Platform, Refresh Years, Redesign Years, MSRP, Style, Structure and Fuel Capacity. 8 MSRP was obtained from web2carz.com and the OEM web sites. Fuel capacity was obtained from OEM spec sheets and product literature.

7 In th eir data update Chrysler provided much of the detailed engine data utilized in the Volpe model input file. These included Fuel Cycle, Fuel Delivery System, Aspiration, Cylinders, Valves/Cylinder, Deactivation, Displacement, Compression Ratio, Max. Horsepo we r, Max. Horsepower RPM, Max. Torque, and Max. Torque RPM. These were copied directly to the engine tab of the Volpe model input file. GM provided similar engine data including Engine Oil Viscosity Fuel Cycle, Fuel Delivery System, Aspiration, Cylinders, Valves/Cylinder, Valvetrain Design, Valve Actuation/Timing, Valve Lift, Deactivation, Displacement, Com pression Ratio, Max. Horsepower, Max. Horsepower RPM, Max. Torque, and Max. Torque RPM. These were copied directly to the engine tab of the Volpe model input file. 8 Daimler, Fiat, GM and Nissan provided Truck Line Name (nameplate) information that could be used to distinguish individual vehicles and their associated characteristics. This level of disaggregation was needed in order to get data on fuel capacity and MSRP for individual vehicles from the OEM web sites or other commercial automotive web sites. Volpe created nameplates to distinguish vehicle types in order to get data on fuel capacity and MSRP for Ford. A comparison of the curb weights and GV WR in the Ford data with those in their product spec sheets (while controlling for drive and engine type) allowed us to make an educated guess as to which of the pickups were for example, 250/350series, regular/crew cab, short box/long box, SRW/DRW. These guesses appear in the column ”Probable Name Plate” of the Volpe input file and are used in the assignment of appropriate values of the various data inputs.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 4 The Structure values (Ladder, Unibody) used b y the CAFE model were added. These were determined from OEM product literature and the automotive press. It should be noted that the new vans such as the Transit in fact utilize a ladder/unibody structure. Ford product literature uses th e term “Uniladder” to describe the structure. Vans based on this structure are noted in the Vehicle Notes column of the NHTSA input file. S tyle values used by

the CAFE model were also added: Chassis Cab, Cutaway , Pickup and Van. 

### 10.1.2.1 **Vehicle Redesign Schedules & Platforms**  10.1.2.2 **Pickup Trucks** Product cadence in the Class 2b and 3 pickup market has historically ranged from 7 - 9 ye ars between major redesigns. However, due to increasing competitive pressures and consumer demands the agency

anticipates that manufacturers will generall y shift to shorter design cycles resembling those of the light duty market. Pickup truck manufacturers in the Class 2b and 3 segments are shown to adopt redesign cycles of six years, allowing two redesigns prior to the end of the proposed regulatory period in 202 7 . 

### 10.1.2.2.1 Fo rd In the 2b / 3 pickup truck market, Ford produces the F250, F350 and F450, currentl y based on the P3 platform. These models adopted the Super Duty moniker in 1999, and began using architecture and product cadence distinct from the F150 light - duty pickup models. The first full redesign of these models occurred in 2008, with smaller redesigns in 2005 and 2011. The agencies estimate that the next major redesign of Ford’s 2b/3 products will occur in or about 2017, trailing Ford’s announced update of a redesigned F150 in its light - duty pickup portfolio, with a more rapid product cadence leading to a subsequent redesign in 2023 and refreshes in 2020 and 2029. 

### 10.1.2.2.2 General Motors Gene ral Motors HD pickup trucks, the Silverado and Sierra HD series, are based on the GMT910 platform and were introduced as a 2007 model. GM has announced a redesigned HD pickup for the 2015 model year. The agencies estimate that, like Ford, GM will adopt an approximate six - ye ar product cadence in the HD truck market, with redesigns in 2015 and 2021. 

### 10.1.2.2.3 F

iat (Ram) The current Ram HD models, on the D2/DJ platform, are anticipated for a major redesign in the 2018 model year, and the agencies estimate that the product will adopt a similar, shorter life cycle of six years, with a subsequent redesign in the 2024 model year.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 5 

### 10.1.2.3 **Vans** The 2b/3 van market has changed markedl y from five years ago. Ford, Nissan, Ram and Daimler have adopted vans of “Euro Van” appearance, and in many cases now use smaller turbocharged gasoline or diesel engines in the place of larger, naturally- aspirated V8s. The 2014 Model Year used in this anal ys is represents a period where most manufacturers, with the exception of General Motors, have recentl y introduced a completely redesigned product after many years. The van segment has historically been one of the slowest to be redesigned of any product segment, with some products going two decades or more between redesigns. Due to new entrants in the field and increased competition, the agencies anticipate that most manufacturers will increase the pace of product redesigns in the van segment, but that they will continue to trail other segments. The cycle time used in this analy sis is approximately ten ye ars between major redesigns, allowing manufacturers’ onl y one major redesign during the regulatory

period. 

### 10.1.2.3.1 General Motors The GM Savana/Chevrolet Express, built on the GMT600 platform, has been produced since 1996 with a facelift in 2003. The van is currently due for a redesign, and while it is unknown when this will occur, the agencies anticipate a major redesign due to strong competitive pressure from other manufacturers will occur in or about 2017, with no further redesigns occurring until aft er 2025. 

### 10.1.2.3.2 Ford 2014 marks the first year in more than three decades that Ford has used a completely new platform for its vans. The Transit replaces the Econoline except in Chassis Cab or cutaway configurations. The agencies anticipate that Ford will gradually shift production volume to the Transit, and will not redesign the Transit until 2025, with one intermediate product freshening. 

### 10.1.2.3.3 F

iat (Ram) The product cycle of the van from the Ram brand has less of a historical precedent. Fiat currentl y offers the Promaster (a variant of the Ducato van sold in other markets). Previousl y Chry sler sold Sprinter vans in an agreement with Daimler from 2003, and had previousl y manufactured its own full - sized van. The Promaster has just been introduced to the US market, and the agencies anticipate that Fiat will offer a refreshed version in 2020 prior to a full redesign in 2025. 

### 10.1.2.3.4 Nissan The Nissan NV launched for the 2012 model using the F - alpha platform shared with the light - duty Nissan Titan pickup truck. Trade publ ications and internet sources suggest the next generation Nissan Titan could debut in model year 2016, and the agencies anticipate that the NV***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 6 van may adopt some of the features and components of the Titan for a mid - cycle freshening of the NV, with a full redesign in 2021. 

### 10.1.2.3.5 Daimler Daimler introduced its current Sprinter van for the 2007 model year on the NCV3 platform. U.S. models received an update across 2014 and 2015, with rear wheel drive models arriving one ye ar ahead of AWD models. The agencies anticipate that Daimler will redesign the Sprinter for 2017 with a subsequent freshening in model year 2021. 

### 10.1.2.4 **Sales Volume Forecast** Since each manufacturer’s required average fuel consumption and GHG levels are sales weighted averages of the fuel econom y/ GH G targets across all model offerings, sales volumes play a critical role in estimating that burden. The CAFE model requires a forecast of sales volumes, at the vehicle model - variant level, in order to simulate the technology application necessary for a manufacturer to achieve compliance in each model year for which outcomes are simulated. For today ’s anal ys is, the agencies relied on the MY 2014 pre - model - year compliance submissions from manufacturers to provide sales volumes at the model level based on the level of disaggregation in which the models appear in the compliance data. However, the agencies only use these reported volumes without adjustment for MY 2014. For all future model years, we combine the manufacturer submissions with sales projections f rom the 2014 Annual Energy Outlook Reference Case and IHS Automotive to determine model variant level sales volumes in future ye ars. 9 Figure 10 - 1 shows the projected sales volumes by class that appear in the 2014 Annual Energy

Outlook as a result of a collection of assumptions about economic conditions, demand for commercial miles traveled, and technology migration from light - duty pickup trucks in response to the concurrent light - duty CAFE/GHG standards. For this anal ys is, the agencies have limited this analy sis fleet to class 2b and 3 HD pickups and vans. However, especiall y considering interactions between the light - duty and HD pickup and van fle ets (e.g., MDPVs being included in the light - duty fleet), the agencies could also evaluat e the potential to analyze the fleets in an integrated fashion.

9 Ta bles from AEO’s forecast are available at http://www.eia.gov/oiaf/aeo/tablebrow ser/ . The agencies also made use of the IHS Automotive Light Vehicle Production Forecast (August 2014).***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 7 **Figure 10**  **- 1**  **AEO2014 Sales Projections for 2b/3 Vehicles**  The projection of total sales volumes for the Class 2b and 3 market segment was based on the total volumes in the 2014 AEO Reference Case. For the purposes of this analysis, the AEO2014 calendar ye ar volumes have been used to represent the corres ponding model - ye ar volumes. While AEO2014 provides enough resolution in its projections to separate the volumes for the Class 2b and 3 segments (see Figure 10 - 1 ), the agencies deferred to the vehicle manufacturers and chose to rely on the relative shares present in the pre - model - ye ar compliance data. The relative sales share by vehicle t ype (van or pickup truck, in this case) was derived from a sales forecast that the agencies purchased from IHS Automotive, and applied to the total volumes in the AEO2014 p rojection. Table 10 - 1 shows the implied shares of the total new 2b/3 vehicle market broken down by manufacturer and vehicle type.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 8 **Table 10**  **- 1**  **IHS Autom otive Market Share Forecast for 2b/3**  **V ehicles**  **MODEL YEAR MARKET SH ARE**  **Manufacturer Style**  **2015 2016**  **2017 2018**  **2019 2020**  **2021** Daimler Van 3% 3% 3% 3% 3% 3% 3% Fiat Van 2% 2% 2% 2% 2% 2% 3% Ford Van 16% 17% 17% 17% 18% 18% 18% General Motors Van 12% 12% 11% 12% 13% 13% 13% Nissan Van 2% 2% 2% 2% 2% 2% 2% Daimler Pickup 0% 0% 0% 0% 0% 0% 0% Fiat Pickup 14% 14% 14% 14% 11% 12% 12% Ford Pickup 28% 27% 30% 30% 30% 27% 26% General Motors Pickup 23% 23% 21% 21% 21% 22% 23% Nissan Pickup 0% 0% 0% 0% 0% 0% 0% Within those broadly

defined market shares, volumes at the manufacturer/model - variant level were constructed by appl yi ng the model - variant’s share of manufacturer sales in the pre model - ye ar compliance data for the relevant vehicle style, and multiplied by the total volume estimated for that manufacturer and that styl e. After building out a set of initial future sales volumes based on the sources described above, the agencies attempted to incorporate new information about changes in sales mix that would not be captured b y either the existing sales forecasts or the simulated technology changes in vehicle platforms. In particular, Ford has announced intentions to phase out their e xisting Econoline vans, gradually shifting volumes to the new Transit platform for some model variants (notably chassis cabs and cutaway s variants) and eliminating offerings outright for complete Econoline vans as earl y as model year 2015. In the case of complete Econoline vans, the volumes for those vehicles were allocated to MY2015 Transit vehicles based on assumptions about likely

production splits for the powertrains of the new Transit platform. The volumes for complete Econoline vans were shifted at ratios of 50 percent , 35 percent , and 15 percent for 3.7 L, 3.5 L Eco - boost, and 3.2 Ldiesel, respectivel y. Within each powertrain, sales were allocated based on the percentage shares present in the pre - model - ye ar compliance data. The chassis cab and cutaw ay

variants of the Econoline were phased out linearly between MY2015 and MY2020, at which time the Econolines cease to exist in any form and all corresponding volume resides with the Transits. 

### 10.1.2.5 **Selected Characteristics of the MY2014 Based Analysis Fleet** The tables below summarize some of the characteristics of the MY2014 based anal ys is fleet for Class 2b and Class 3 trucks. Table 10 - 2 s hows production by manufacturer and indicates that Ford is dominant with 52 percent of this market.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 9 **Table 10**  **- 2**  **Estim ated MY2014 Production by Manufacturer**  **MANUFACTURER PRODUCTION**  **PERCENT** Daimler 25,327 4.0% Fiat 138,902 21.8% Ford 330,919 51.9% General Motors 129,435 20.3% Nissan 13,526 2.1% Total 638,109 100.0% Table 10 - 3 sho ws production by class with 80 percent of production in class 2b, those trucks with a GVW between 8,501 and 10,000 lbs. **Table 10**  **- 3**  **Estim ated MY2014 Production by Class**  **GVW CLASS PRODUCTION**  **PERCENT** 2b (8,501 - 10,000 lbs.) 506,989 79.5% 3 (10,001 - 14,000 lbs.) 131,120 20.5%

Total 638,109 100.0% Table 10 - 4 sh ows production by style or body

typ e. Pickup trucks make up 52 percent of production and vans 42 percent of production. **Table 10**  **- 4**  **Estim ated MY2014 Production by Vehicle Style**  **STYLE PRODUCTION**  **PERCENT** Chassis Cab 19,724 3.1% Cuta way 20,539 3.2% Pickup 333,100 52.2% Van 264,746 41.5% Total 638,109 100.0% Table 10 - 5 shows production by engine type. Diesel powered trucks make up a significant share (40 percent) of this market in comparison to light duty vehicles.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 10 **Table 10**  **- 5**  **Estim ated MY2014 Production by Engine Type**  **ENGINE TYPE PRODUCTION**  **PERCENT** Diesel 252,744 39.6% Gasoline 105,604 16.5% FFV 279,761 43.8% Total 638,109 100.0% Table 10 - 6 s hows production by drive type with an almost equal division between two wheel drive (55 percent) and four wheel drive (45 percent ). **Table 10**  **- 6**  **Estim ated MY2014 Production by Drive**  **DRIVE PRODUCTION**  **PERCENT** 4WD 286,122 44.8% FWD 23,309 3.7% RWD 328,678 51.5% Total 638,109 100.0% The following tables show some of the characteristics of the baseline anal ysis fleet at the manufacturer level . Table 10 - 7 and Table 10 - 8 show production by manufacturer for class 2b and class 3 trucks respectively . As noted above Ford is the dominant manufacturer with 52 percent of the market in both class 2b and class 3 trucks. While Fiat and General Motors have comparable shares of the class 2b market (20 percent and 22 percent respectively), Fiat (at 31 percent ) has a significantly larger share of the class 3 market than General Motors (at 13 percent ). **Table 10**  **- 7**  **Estim ated MY2014 Production Class 2b by Manufacturer**  **MANUFACTURER PRODUCTION**  **PERCENT** Daimler 19,556 3.9% Fiat 98,722 19.5% Ford 262,687 51.8% General Motors 112,498 22.2% Nissan 13,526 2.7% Total 506,989 100.0%***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 11 **Table 10**  **- 8**  **Estim ated MY2014 Production Class 3 by Manufacturer**  **MANUFACTURER PRODUCTION**  **PERCENT** Daimler 5,771 4.4% Fiat 40,180 30.6% Ford 68,232 52.0% General Motors 16,937 12.9% Nissan - 0.0% Total 131,120 100.0% As noted above pickup trucks were the dominant body styl e in Class 2b and 3 trucks. Table 10 - 9 shows pickup truck production by manufacturer. Onl y three manufactures share this market with Ford the leader at 43 percent , followed by Fiat at 35 percent and General Motors at 22 percent . **Table 10**  **- 9**  **Estim ated MY2014 Production Pickups by Manufacturer**  **MANUFACTURER PRODUCTION**  **PERCENT** Daimler - 0.0% Fiat 115,593 34.7% Ford 142,580 42.8% General Motors 74,927 22.5% Nissan - 0.0% Total 333,100 100.0% All five manufactures share the Class 2b and 3 van market. Table 10 - 10 s hows van production by

manufacturer. Ford is again dominant with 57 percent of the market followed by General Motors at 21 percent with the remainder divided among Fiat, Daiml er and Nissan.

**Table 10**  **- 10**  **Estim ated MY2014 Production Vans by Manufacturer**  **MANUFACTURER PRODUCTION**  **PERCENT** Daimler 21,900 8.3% Fiat 23,309 8.8% Ford 151,503 57.2% General Motors 54,508 20.6% Nissan 13,526 5.1% Total 264,746 100.0% Table 10 - 11 and Table **10 -**  **12** give an indication of the significance of diesel powered trucks in the class 2b and 3 market. Table 10 - 11 shows the distribution of diesel trucks b y manufacturer. Ford is the leader at 40 percent followed by Fiat at 34 percent .***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 12 Table **10 -**  **12** shows diesel production as a percent of t otal production for each manufacturer. At either end of the spectrum are Nissan at 0 percent and Daimler at 100 percent . Of the producers with significant market share Fiat leads with 62 percent of its production in diesels, followed b y General Motors at 32 percent and Ford at 30 percent .

**Table 10**  **- 11**  **Estim ated MY2014 Production Diesel Powered Trucks by Manufacturer** 

**MANUFACTURER PRODUCTION**  **PERCENT** Daimler 25,327 10.0% Fiat 86,124 34.1% Ford 100,208 39.6% General Motors 41,085 16.3% Nissan - 0.0% Total 252,744 100.0% **Table 10**  **- 12**  **Estim ated MY2014 Diesel Penetration by Manufacturer**  **MANUFACTURER DIESEL**  **PRODUCTION TOTAL** 

**PRODUCTION PERCENT** 

**DIESEL** Daimler 25,327 25,327 100.0% Fiat 86,124 138,902 62.0% Ford 100,208 330,919 30.3% General Motors 41,085 129,435 31.7% Nissan - 13,526 0.0% Total 252,744 638,109 39.6% The resultant analy sis fleet is provided in detail at NHTSA’s web site, along with all other inputs to and outputs from today ’s anal ys is. 

### 10.2 **CAFE Model Analysis of Regulatory Alternatives for**  **HD Pickups and**  **Vans** EPA and NHTSA have evaluated a range of potential regulatory alternatives since we are considering the proposal of new class 2b and 3 pickup and van fuel consumption and GHG standards to follow those already established through model ye ar 2018. The agencies estimated the extent to which manufacturer s might add fuel - saving (and, therefore, CO 2 - reducing) technologies under each regulatory alternative, including the no - action alternative defined b y Phase 1 standards. NHTSA also used the CAFE model to estimate the extent to which this additional technol ogy

would incrementall y (compared to the no - action alternative) impact costs to manufacturers and vehicle buyers, reduce fuel consumption and greenhouse gas emissions, provide economic benefits and reduce costs to vehicle owners and society. The remainder of this section presents the regulatory alternatives the agencies have considered, summarizes the anal ys is, and explains the selection of the preferred alternative defined by today ’s proposed standards.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 13 As discussed above, the agencies are proposing standards defined b y fuel consumption and GHG targets that continue through model ye ar 2020 unchanged from model year 2018, and then increase in stringency at an annual rate of 2.5 percent through model year 202 7 . In addition to this regulatory

alternative, th e agencies also considered a no - action alternative under which standards remain unchanged after model year 2018, as well as three other alternatives, defined b y annual stringency

increases of (1) 2. 0 percent , (2), 3.5 percent , and (3) 4 .0 percent during mo del ye ars 2021 - 2025. For each of the “action alternatives” (i.e., those involving stringency increases beyond the no - action alternative), the annual stringency increases are applied as follows: an annual stringency

increase of r is applied b y multiplying the model ye ar 2020 target functions (identical to those applicable to model year 2018) by 1

– r to define the model year 2021 target functions, multiplying the model year 2021 target functions by 1

– r to define the model year 2022 target functions, continuing through 2025 for all alternatives except for the preferred Alternative 3 which extends through 2027 . In summary , the agencies have considered the following five regulatory alternatives:

REGULATORY ALTERNATIVE ANNUAL STRINGENCY IN CREASE 2019 - 2020 2021 - 2025 2026 - 2027 1: No Action None None None 2: 2.0%/y None 2.0% None 3: 2.5%/y None 2.5% 2.5% 4: 3.5%/y None 3.5% None 5: 4.0%/y None 4.0% None 

### 10.2.1 **Evaluation of the Regulatory Alternatives**  **.** To conduct an anal ys is of potential standards for HD pickups and vans, the agencies have applied DOT’s Corporate Average Fuel Econom y (CAFE) Compliance and Effects Modeling Sy stem (sometimes referred to as “the CAFE model” or “the Volpe model”), which DOT’s Volpe National Transportation Systems Center (Volpe Center) developed, maintains, and applies to support NHTSA CAFE anal ys es and rulemakings. DOT developed the model in 2002 to support the 2003 issuance of CAFE standards for MYs 2005 - 2007 light trucks. DOT has since significantl y expanded and refined the model, and has applied the model to support every ensuing CAFE rulemaking:  2006: MYs 2008 - 2011 light trucks  2008: MYs 2011 - 2015 passenger cars and light trucks (final rule prepared but withheld)  2009: MY 2011 passenger cars and light trucks  2010: MYs 2012 - 2016 passenger cars and light trucks (joint rulemaking with EPA)  2012: MYs 2017 - 2021 passenger cars and light trucks (joint rulemaking with EPA) Past analy ses conducted using the CAFE model have been subjected to extensive and detailed review and comment, much of which has informed the model’s expansion and refinement. NHTSA’s use of the model was considered and supported in 2007 litigation (CBD v. NHTSA), and the model has been subjected to formal peer review and review by the General Accounting Off

ice (GAO) and National Research Council (NRC). NHTSA makes public the model, source code, and

—except insofar as doing so would compromise confidential business***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 14 information (CBI) manufacturers have provided to NHTSA

—all model inputs and outputs underlying published rulemaking anal ys es. 10 Although the CAFE model can also be used for more aggregated anal ys is (e.g., involving “representative vehicles”, single - ye ar snapshots, etc.), NHTSA designed the model with a view toward (a) detailed simulation of manufacturers’ potential actions given a defined set of standards, followed b y (b) calculation of resultant impacts and economic costs and benefits. The model is intended to describe actions manufacturers could take in light of defined standards and other input as sumptions and estimates, not to predict actions manufacturers will take. As a starting point, the model makes use of an input file defining the anal ys is fleet

—that is, a set of specific vehicle models (e.g., To yota Tacoma) and model configurations (e.g., T oyota Tacoma with 4.0 - liter V6 engine, 4WD, and 5 - speed manual transmission) estimated or assumed to be produced b y each manufacturer in each model year to be included in the anal ys is. The anal ys is fleet includes key engineering attributes (e.g., curb wei ght, payload and towing capacities, dimensions, presence of various fuel - saving technologies) of each vehicle model, engine, and transmissions, along with estimates or assumptions of future production volumes. It also specifies the extent to which specifi c vehicle models share engines, transmissions, and vehicle platforms, and describes each manufacturer’s estimated or assumed product cadence ( i.e. , timing for freshening and redesigning different vehicles and platforms). This input file also specifies a p ay

back period used to estimate the potential that each manufacturer might appl y technology

to improve fuel econom y be yond levels required b y standards. A second input file to the model contains a variety of contextual estimates and assumptions. Some of th ese inputs, such as future fuel prices and vehicle survival and mileage accumulation (versus vehicle age), are relevant to estimating manufacturers’ potential application of fuel - saving technologies. Some others, such as fuel density and carbon content, v ehicular and upstream emission factors, the social cost of carbon dioxide emissions, and the discount rate, are relevant to calculating phys ical and economic impacts of manufacturers’ application of fuel - saving technologies. A third input file contains estimates and assumptions regarding the future applicability , availability , efficacy , and cost of various fuel - saving technologies. Efficacy is expressed in terms of the percentage reduction in fuel consumption, cost is expressed in dollars, and both efficac y and cost are expressed on an incremental basis (i.e., estimates for more advanced technologies are specified as increments beyond less advanced technologies). The input file also includes “s ynergy factors” used to make adjustments accounting for the pot ential that some combinations of technologies may result fuel savings or costs different from those indicated by incremental values. Finally , a fourth model input file specifies standards to be evaluated. Standards are defined on ye ar - by- ye ar basis separately for each regulatory

class (passenger cars, light trucks, and heav y- duty pickups and vans). Regulatory alternatives are specified as discrete scenarios,

10 Analyses can be found at http://www.nhtsa.gov/fuel - economy***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 15 with one scenario defining the no - action alternative or “baseline”, all other scenarios defining regulatory

alternatives to be evaluated relative to that no - action alternative. Given these inputs, the model estimates each manufacturer’s potential ye ar - by- ye ar application of fuel - saving technologies to each engine, transmission, and vehicle. Subj ect to a range of engineering and planning - related constraints (e.g., secondary axle disconnect can’t be applied to 2 - wheel drive vehicles, many major technologies can only be applied practicabl y as part of a vehicle redesign, and applied technologies carry forward between model years), the model attempts to apply

technology

to each manufacturers’ fleet in a manner that minimizes “effective costs” (accounting, in particular, for technology costs and avoided fuel outlay s), continuing to add improvements as l ong as doing so would help toward compliance with specified standards or would produce fuel savings that “pay back” at least as quickly

as specified in the input file mentioned above. Having estimated the extent to which each manufacturer might add fuel - saving technologies under each specified regulatory alternative, the model calculates a range of phy sical impacts, such as changes in highway travel (i.e., VMT), changes in fleetwide fuel consumption, changes in highway

fatalities, and changes in vehicular and upstream greenhouse gas and criteria pollutant emissions. The model also applies a variety of input estimates and assumptions to calculate economic costs and benefits to vehicle owners and society, based on these ph ys ical impacts. T his analy sis reflect s several changes made to the model since 2012, when NHTSA used the model to estimate the effects, costs, and benefits of final CAFE standards for light - duty vehicles produced during MYs 2017 - 2021, and augural standards for MYs 2022 - 2025. Some of these ch anges specifically enable anal ys is of potential fuel consumption standards (and, hence, related CO 2 emissions standards harmonized with fuel consumption standards) for heavy- duty pickups and vans; other changes implement more general improvements to the mo del. Key changes include the following:  Expansion and restructuring of model inputs, compliance calculations, and reporting to accommodate standards for heav y- duty pickups and vans, including attribute - based standards involving targets that vary with “wor

k factor”.  Explicit calculation of test weight, taking into account test weight “bins” and differences in the definition of test weight for light - duty vehicles (curb weight plus 300 pound) and heav y- duty pickups and vans (average of GVWR and curb weight).  Procedures to estimate increases in payload when curb weight is reduced, increases in towing capacity if GVWR is reduced, and calculation procedures to correspondingl y update calculated work factors.  Expansion of model inputs, procedures, and outputs to ac commodate technologies not included in prior anal ys es.  Changes to the algorithm used to apply technologies, enabling more explicit accounting for shared vehicle platforms and adoption and “inheritance” of major engine changes.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 16  Expansion of the Monte Carlo simulation procedures used to perform probabilistic uncertainty analysis. These changes are reflected in updated model documentation available at NHTSA’s web site, the documentation also providing more information about the model’s purpose, scope, structure, design, inputs, operation, and outputs 

### 10.2.1.1 **Accounting for Product Cadence** Past comments on the CAFE model have stressed the importance of product cadence

— i.e., the development and periodic redesign and freshening of vehicles

—in terms of involving technic al, financial, and other practical constraints on applying new technologies, and DOT has steadily made changes to the model with a view toward accounting for these considerations. For example, earl y versions of the model added explicit “carrying forward” of applied technologies between model ye ars, subsequent versions applied assumptions that most technologies would be applied when vehicles are freshened or redesigned, and more recent versions applied assumptions that manufacturers would sometimes apply te chnology

earlier than “necessary” in order to facilitate compliance with standards in ensuing model years. Thus, for example, if a manufacturer is expected to redesign many of its products in model years 2018 and 2023, and the standard’s stringency

increa ses significantly

in model year 2021, the CAFE model will estimate the potential that the manufacturer will add more technology than necessary

for compliance in MY 2018, in order to carry those product changes forward through the next redesign and contribute to compliance with the MY 2021 standard. The model also accommodates estimates of overall limits (expressed as “phase - in caps” in model inputs) on the rates at which manufacturers’ may practicably

add technology

to their respective fleets. So, for ex ample, even if a manufacturer is expected to redesign half of its production in MY 2016, if the manufacturer is not already producing any strong h yb rid electric vehicles (SHEVs), a phase - in cap can be specified in order to assume that manufacturer will sto p applying SHEVs in MY 2016 once it has done so to at least 3 percent of its production in that model year. After the light - duty rulemaking anal ys is accompanying the 2012 final rule regarding post - 2016 CAFE standards and related GHG emissions standards, DO T staff began work on CAFE model changes expected to better reflect additional considerations involved with product planning and cadence. These changes, summarized below, interact with preexisting model characteristics discussed above. 

### 10.2.1.2 **Platforms & Technol ogy**  The term “platform” is used loosely in industry , but generall y refers to a common structure shared b y a group of vehicle variants. The degree of commonality varies, with some platform variants exhibiting traditional “badge engineering” where two produ cts are differentiated b y little more than insignias, while other platforms be used to produce a broad suite of vehicles that bear little outer resemblance to one another.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 17 Given the degree of commonality between variants of a single platform, manufacturers do not have complete freedom to apply technology

to a vehicle: while some technologies (e.g. low rolling resistance tires) are very nearl y “bolt - on” technologies, others involve substantial changes to the structure and design of the vehicle, and therefore necessaril y are constant between vehicles that share a common platform. DOT staff has, therefore, modified the CAFE model such that all mass reduction and aero technologies are forced to be constant between variants of a platform. Within the analysis fleet, each vehicle is associated with a specific platform. As the CAFE model applies technology , it first defines a platform “leader” as the vehicle variant of a platform with the highest technology utilization vehicle of mass reduction and aerody namic t echnologies. As the vehicle applies technologies, it effectivel y harmonizes to the highest common denominator of the platform. If there is a tie, the CAFE model begins applying aerodynamic and mass reduction technology to the vehicle with the lowest average sales across all available model ye ars. If there remains a tie, the model begins by choosing the vehicle with the highest average MSRP across all available model years. The model follows this formulation due to previous market trends suggesting that many technologies begin deploy ment at the high end, low - volume end of the market as manufacturers build their confidence and capability in a technology , and later expand the technology across more mainstream product lines. In the HD pickup and van market, there is a relatively small amount of diversity in platforms produced b y m anufacturers: typicall y 1 - 2 truck platforms and 1 - 2 van platforms. However, accounting for platforms will take on greater significance in future anal ys es involving the light - duty fl eet, and the agency requests comments on the general use of platforms within CAFE rulemaking. 

### 10.2.1.3 **Engine and Transmission Inheritance** In practice, manufacturers are limited in the number of engines and transmissions that they produce. Ty

picall y a manufacturer produces a number of engines

— perhaps six or eight engines for a large manufacturer

—and tunes them for slight variants in output for a variety of car and truck applications. Manufacturers limit complexity in their engine portfolio for much the same reason as they

limit complexity

in vehicle variants: they

face engineering manpower limitations, and supplier, production and service costs that scale with the number of parts produced. In previous usage of the CAFE model, engines and transmissions in individual models were allowed relative freedom in technology application, potentially

leading to solutions that would, if followed, involve unaccounted - for costs associated with increased complexity in the product portfolio. The lack of a constraint in this area allowed the model to apply different levels of technology

to the engine in each vehicle at the time of redesign or refresh, independent of what was done to other vehicles using a previously identical engine. In the current version of the CAFE model, engines and transmissions that are shared between vehicles must apply the same levels of technology in all technologies dictated by engine or transmission inheritance. This forced adoption is referred to as “engine inheritance” in the model documentation.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 18 As with platform - shared technologies, the model first chooses an “engine leader” among vehicles sharing the same engine. The leader is selected first by the vehicle with the lowest average sales across all available model years. If there is a tie, the vehicle with the highest average MSRP across model years is chosen. The model applies the same logic with respect to the application of transmission changes. As with platforms, this is driven by the concept that vehicle manufacturers typically deploy new technol ogies in small numbers prior to deploying widely across their product lines. 

### 10.2.1.4 **Interactions between Regulatory Classes** Like earlier versions, the current CAFE model provides for integrated anal ysis spanning different regulatory

classes, accounting both for standards that appl y separately to different classes and for interactions between regulatory classes. L ight vehicle CAFE standards are specified separately

for passenger cars and light trucks. However, there is considerable sharing between these two regulatory classes. Some specific engines and transmissions are used in both passenger cars and light trucks, and some vehicle platforms span these regulatory classes. For example, some sport - utility vehicles are offered in 2WD versions classified as passeng er cars and 4WD versions classified as light trucks. Integrated anal ys is of manufacturers’ passenger car and light truck fleets provides the ability to account for such sharing and reduce the likelihood of finding solutions that could involve impractical levels of complexity in manufacturers’ product lines. In addition, integrated anal ys is provides the ability to simulate the potential that manufactures could earn CAFE credits by over complying with one standard and use those credits toward compliance with the other standard (i.e., to simulate credit transfers between regulatory

classes). HD pickups and vans are regulated separately from light - duty

vehicles. While manufacturers cannot transfer credits between light - duty and MDHD classes, there is some sharing of engineering and technology between light - duty

vehicles and HD pickups and vans. For example, some passenger vans with GVWR over 8,500 pounds are classified as medium duty passenger vehicles (MDPVs) and thus included in manufacturers’ light - duty tr uck fleets, while cargo vans sharing the same nameplate are classified as HD vans. While this analysis examines the HD pickup and van fleet in isolation, as a basis for anal ys is supporting the planned final rule, the agencies intend to develop an overall anal ys is fleet spanning both the light - duty and HD pickup and van fleets. Doing so could show some technology

“spilling over” to HD pickups and vans due, for example, to the application of technology

in response to current light - duty standards. More gener ally , modeling the two fleets together should tend to more realistically limit the scope and complexity of estimated compliance pathway s. NHTSA anticipates that the impact of modeling a combined fleet will primarily arise from engine - transmission inheritan ce. While platform sharing between the light - duty and MD pickup and van fleets is relatively small (MDPVs aside), there are a number of instances of engine and transmission sharing across the two fleets. When the fleets are modeled together, the agencies anticipate that engine inheritance will be implemented across the combined fleet, and therefore onl y one engine - transmission leader can be defined across the combined fleet. As with***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 19 the fleets separatel y, all vehicles using a shared engine/transmission w ould automatically adopt technologies adopted b y the engine - transmission leader. 

### 10.2.1.5 **Phase -**  **In Caps** The CAFE model retains the ability to use phase - in caps (specified in model inputs) as proxies for a variety of practical restrictions on technology application. Unlike vehicle - specific restrictions related to redesign, refreshes or platforms/engines, phase - in caps constrain technology

application at the vehicle manufacturer level. They are intended to reflect a manufacturer's overall resource capacity available for implementing new technologies (such as engineering and development personnel and financial resources), thereb y ensuring that resource capacity is accounted for in the modeling process. In previous CAFE rulemakings, redesign/refresh schedules and phase - in caps were the primary mechanisms to reflect an OEM's limited pool of available resources during the rulemaking time frame and the years leading up to the rulemaking time frame, especiall y in ye ars where many models may be scheduled for refresh or redes ign. The newl y- introduced representation platform - , engine - , and transmission - related considerations discussed above augment the model’s preexisting representation of redesign cycles and accommodation of phase in caps. Considering these new constraints, inputs for today ’s anal ys is de - emphasize reliance on phase - in caps. In this application of the CAFE model, phase - in caps are used onl y for the most advanced technologies included in the anal ys is, i.e., SHEVs and lean - burn GDI engines, considering that thes e technologies are most likely to involve implementation costs and risks not otherwise accounted for in corresponding input estimates of technology cost. For these two technologies, the agencies have applied caps that begin at 3 percent (i.e., 3 percent o f the manufacturer’s production) in MY 2017, increase at 3 percent annually during the ensuing nine years (reaching 30 percent in the MY 2026), and subsequentl y increasing at 5 percent annually for four ye ars (reaching 50 percent in MY 2030). Note that th e agencies did not feel that lean - burn engines were feasible in the timeframe of this rulemaking, so decided to reject any model runs where they were selected. Due to the cost ineffectiveness of this technology , it was never chosen. 

### 10.2.1.6 **Impact of Vehicle Techn ology Application Requirements**  Compared to prior analy ses of light - duty standards, these model changes, along with characteristics of the HD pickup and van fleet result in some changes in the broad characteristics of the model’s application of technology t o manufacturers’ fleets. First, since the number of HD pickup and van platforms in a portfolio is ty picall y small, compliance with standards may appear especiall y “lump y” (compared to previous applications of the CAFE model to the more highl y segmented li ght - duty fleet), with significant over compliance when widespread redesigns precede stringency

increases, and/or significant application of carried - forward (aka “banked”) credits. Second, since the use of phase - in caps has been de - emphasized and manufacturer technology

deploy ment remains tied strongl y to estimated product redesign and freshening***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 20 schedules, technology

penetration rates may

jump more quickly

as manufacturers appl y technology

to high - volume products in their portfolio. By design, restrictions that enforce commonality of mass reduction and aerody namic technologies on variants of a platform, and those that enforce engine inheritance, will result in fewer vehicle - technology combinations in a manufacturer’s future modeled fleet. These restrictions are expected to more accuratel y capture the true costs associated with producing and maintaining a product portfolio. 

### 10.2.1.7 **Example of Technology Application Estimated using Current Model and Inputs**  The example presented below illustrates how some of aspects of the current model and inputs impact estimation of technology application by

a manufacturer within the context of a specified set of standards, focusing here on the model’s estimate of GM’s technology application under the 4 .0 percent/y

regulatory

alternative (Alternative 5) . Overall results for GM and other manufacturers are summarized below, after discussion of the anal ys is fleet used for today ’s anal ys is. Results for GM clearl y reflect the anal ys is fleet’s inclusion of just one HD pickup platform with redesigns estimated to occur in MYs 2021 and 2026 and one HD van platform with a redesign estimated to occur in MY 2020. The anal ys is suggests that GM could take some advantage of credit carry - forward provisions (e.g., to cover a shortfall projected to oc cur in MY 2016, when the HD pickup and van fleet first includes some vehicles not previously subject to chassis dy

namometer testing), but that GM could need to significantl y over comply with standards during MYs 2021 - 2024 in order take full advantage of th e estimated MY 2021 HD pickup redesign and thereby carry forward enough technology to remain in compliance through MY 2030 (the last model year included in today ’s anal ys is). The results also reflect that credits earned during MY 2021 - 2024 expire before MY 2030, and that the Express/Savana vans inherit a new transmission in MY 2027.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 21 **Figure 10**  **- 2**  **Example of technology application during redesigns** Specific steps estimated to be taken when these platforms are redesigned and freshened are as follows: MY 2017 Express/Savana Redesign  4.8 liter gasoline engine: replace with smaller turbocharged direct injection engine  6.0 liter gasoline engine: add lower- friction lubricants, engine friction reduction, cylinder de activation, variable valve actuation  All Express/Savana vans: apply 5 percent mass reduction, aerodynamic improvements, electric power steering, improved accessories, integrated starter/generators, and low - drag brakes  For Express/Savana vans, vs. MY 2014 o additional $3,425 - $4,473 o avoided 0.94 - 2.05 gal./100 mi. MY2018 Sierra/Silverado Freshening***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 22  6.0 liter gasoline engine: apply lower - friction lubricants and engine friction reduction  All Sierra/Silverado pickups: apply 5 percent mass reduction, aerody namic improvements, and electric power steering, and improved accessories  For Sierra/Silverado pickups, vs. MY 2014 o additional $383 - $643 o avoided 0.15 - 0.50 gal./100 mi. MY 2020 Express/Savana Freshening  Carry forward changes from MY 2017 (and through 2018 - 2019)  All Express/Savana vans: apply reduced rolling resistance tires  For Express/Savana vans, vs. MY 2014 o additional $3,128 - $4,033 o avoided 1.01 - 2.11 gal./100 mi. MY 2021 Sierra/Silverado Redesign  Carry forward changes from MY 2018 (and through 2019 - 2020)  6.0 liter gasoline engine: apply cylinder deactivation and variable valve actuation  6.6 liter diesel engine: engine friction reduction and improved turbocharging  All Sierra/Silverado pickups: apply 8 - speed automatic transmission, 10 percent mass reduction, further aerodynamic improvements, low - drag brakes, secondary axle disconnect (on all 4WD units), low rolling resistance tires  76 percent of Sierra/Silverado pickups: appl y integrated starter - generators  24 percent of Sierra/Silverado pickups: appl y stron g h ybrid - electric s ys tems (37k units)  For Sierra/Silverado pickups, vs. MY 2014 o additional $3,197 - $5,805 o avoided 1.12 - 3.09 gal./100 mi. MY 2027 Express/Savana Redesign  Carry forward changes from MY 2020 (and through 2021 - 2026)  All Express/Savana vans: apply 8 - speed transmission (inherited from 2021 Sierra/Silverado)  1.4 percent of Express/Savana vans: appl y strong hybrid - electric s ys tems (1.4k units)  For Express/Savana vans, vs. MY 2014 o additional $3,086 - $5,226 o avoided 1.03 - 3.29 gal./100 mi.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 23 As discussed above, these results provide an estimate, based on anal ys is inputs, of one way GM could add fuel - saving technologies to its HD pickups and vans under one of the regulatory

alternatives considered here, and are not a prediction of what GM would do under this regulatory

alternative. In addition, it should be recognized that specific results vary among manufacturers and among regulatory alternatives (and under different analy tical inputs). Still, the example should serve to illustrate how variou s inputs can impact results given the CAFE model’s approach to estimating how fuel - saving technologies might be added to manufacturers’ fleets. 

### 10.2.1.8 **Accounting for Test Weight, Payload, and Towing Capacity** As mentioned above, NHTSA has also revised the CAFE mod el to explicitly account for the regulatory

“binning” of test weights used to certify light - duty fuel economy and HD pickup and van fuel consumption for purposes of evaluating fleet - level compliance with fuel econom y and fuel consumption standards. For HD pickups and vans, test weight (TW) is based on adjusted loaded vehicle weight (ALVW), which is defined as the average of gross vehicle weight rating (GVWR) and curb weight (CW). 11 TW values are then rounded, resulting in TW “bins”:

ALVW ≤ 4,000 lb.: TW rounded to nearest 125 lb. 4000 lb. < ALVW ≤ 5,500 lb.: TW rounded to nearest 250 lb. ALVW > 5,500 lb.: TW rounded to nearest 500 lb. This “binning” of TW is relevant to calculation of fuel consumption reductions accompanyi ng mass reduction. Model inputs for mass reduction (as an applied technology) are expressed in terms of a percentage reduction of curb weight and an accompanying estimate of the percentage reduction in fuel consumption, setting aside rounding of test weight. Therefore, to account for rounding of test weight, NHTSA has modified these calculations as follows: ∆

_ = ∆ × ∆

_ ∆ Where : ∆CW = % change in curb weight (from model input), ∆FC unrounded_TW = % change in fuel consumption (from m odel input), without TW rounding, ∆TW = % change in test weight (calculated), and ∆FC rounded_TW = % change in fuel consumption (calculated), with TW rounding.

11 Or, equivalently, CW + 1⁄2 payload, where payload = GVWR

– CW.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 24 As a result, some applications of vehicle mass reduction will produce no compliance benefit at al l, in cases where the changes in ALVW are too small to change test weight when rounding is taken into account. On the other hand, some other applications of vehicle mass reduction will produce significantly more compliance benefit than when rounding is no t taken into account, in cases where even small changes in ALVW are sufficient to cause vehicles’ test weights to increase b y, e.g., 500 pounds when rounding is accounted for. Model outputs now include initial and final TW, GVWR, and GCWR (and, as before, CW) for each vehicle model in each model ye ar, and the agencies invite comment on the extent to which these changes to account explicitly

for changes in TW are likely to produce more realistic estimates of the compliance impacts of reductions in vehicle m ass. In addition, considering that the regulatory alternatives in the agencies’ analy sis all involve attribute - based standards in which underl ying fuel consumption targets vary with “work factor” (defined b y the agencies as the sum of three quarters of payload, one quarter of towing capacity , and 500 lb. for vehicles with 4WD), NHTSA has modified the CAFE model to appl y inputs defining shares of curb weight reduction to be “returned” to payload and shares of GVWR reduction to be returned to towing capacity. The standards’ dependence on work factor provides some incentive to increase payload and towing capacity , both of which are buyer - facing measures of vehicle utility. In the agencies’ judgment, this provides reason to assume that if vehicle mass is reduc ed, manufacturers are likel y to “return” some of the change to payload and/or towing capacity . For this anal ys is, the agencies have applied the following assumptions: GVWR will be reduced by half the amount b y which curb weight is reduced. In other words , 50 percent of the curb weight reduction will be returned to payload. GCWR will not be reduced. In other words, 100 percent of any GVWR reduction will be returned to towing capacity . GVWR/CW and GCWR/GVWR will not increase beyond levels observed among the majority of similar vehicles (or, for outlier vehicles, initial values): **MAXIMUM RATIOS ASSUM ED ENABLED BY MASS** 

**REDUCTION Group**  **GVWR/CW GCWR/GVWR**  unibody 1.75 1.50 gasoline pickups > 13k GVWR 2.00 1.50 other gasoline pickups 1.75 2.25 diesel SRW pickups 1.75 2.50 All other 1.75 2.25 The first of two of these inputs are specified along with standards for each regulatory alternative, and the GVWR/CW and GCWR/GVWR “caps” are specified separately for each vehicle model in the analysis fleet. In addition, DOT has changed the model to prevent HD pickup and van GVWR from falling below 8,500 pounds when mass reduction is applied (because doing so would cause vehicles to be reclassified as light - duty vehicles), and to treat any additional mass for hy brid***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 25 electric vehicles as reducing payload b y the same amount (e.g., if adding a strong HEV package to a vehicle involves a 350 pound penalty , GVWR is assumed to remain unchanged, such that payload is also reduced by 350 pounds). The agencies invite comm ent on these methods for estimating how changes in vehicle mass may impact fuel consumption, GVWR, and GCWR, and on corresponding inputs to today ’s anal ys is. 

### 10.2.2 **What Impacts Did the Agencies’ Analysis Show for Different Regulatory Alternatives**  10.2.2.1 **Industry Impac ts**  As discussed above, the agencies’ anal ys is fleet provides a starting point for estimating the extent to which manufacturers might add fuel - saving (and, therefore, CO 2 - avoiding) technologies under various regulatory alternatives, including the no - action alternative that defines a baseline relative to which to measure estimated impacts of new standards. The anal ys is fleet is a forward - looking projection of production of new HD pickups and vans, holding vehicle characteristics (e.g., technology content and fuel consumption levels) constant at model year 2014 levels, and adjusting production volumes based on recent DOE and commercially- available forecasts. This anal ys is fleet includes some significant changes relative to fleet information underlying anal ys i s supporting the establishment of Phase 1 standards applicable starting in model year 2014; in particular, the anal ys is fleet includes some new HD vans (e.g., Ford’s Transit and Fiat/Chry sler’s Promaster) that are considerable more fuel - efficient than HD v ans these manufacturers have previously produced for the U.S. market. While the proposed standards are scheduled to begin in model year 2021, the requirements they

define are likel y to influence planning decisions made b y manufacturers several ye ars before they

begin, as illustrated by

example above. This is true in light - duty planning, but accentuated by the comparativel y long redesign cycles and small number of models and platforms offered for sale in the 2b/3 market segment. Additionally , manufacturers will respond to the cost and efficacy of available fuel consumption improvements, the price of fuel, and the requirements of the Phase 1 standards that specify maximum allowable average fuel consumption improvements and GHG levels for MY2014 - MY2018 vehicl es (the final standard for MY2018 is held constant for model years 2019 and 2020). The forward - looking nature of product plans that determine which vehicle models will be offered in the model years affected b y the proposed standards lead to additional tec hnology application to vehicles in the analy sis fleet that occurs in the ye ars prior to the start of the proposed standards. From the industry perspective, this means that manufacturers will incur costs to comply with the proposed standards in the baselin e and that the total cost of the proposed regulations will include some costs that occur prior to their start, and represent incremental changes over a world in which manufacturers will have already modified their vehicle offerings compared to today .***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 26 **Table 10**  **- 13**  **MY 2021**  **B aseline**  **C osts for**  **M anufacturers in 2b/3**  **M arket**  **S egment**  **in the Dynamic Baseline, or Alternative 1b**  **MANUFACTURER AVERAGE**  **TECHNOLOGY COST ($)**  **TOTAL COST**

**INCREASE ($M)** Chrysler/Fiat 275 27 Daimler 18 0 Ford 258 78 General Motors 782 191 Nissan 282 3 Industry 442 300 As Table 10 - 13 shows, the industry as a whole is expected to add about $440 of new technology

to each new vehicle model by 2021 under the no - action alternative defined b y the Phase 1 standards. Reflecting differences in projected product offerings in the anal ys is fleet, some manufacturers (notably Daimler) are significantly less constrained by

the Phase 1 standards than others and face lower cost increases as a result. General Motors (GM) shows the largest increase in average vehicle cost, but results for GM’s closest competitors (Ford and Chry sler/Fiat) do not include the costs of their recent van redesigns, which are already present in the anal ys is fleet (discussed in greater detail below). The above results reflect the assumption that manufacturers having achieved compliance with standards might act as if buyers are willing to pay for further fuel consumption improvements that “pay

back” within 6 months. It is also possible that manufacturers will choose not to migrate cost - effective technologies to the 2b/3 market segment from similar vehicles in the light - duty market. To examine this possibility , all regulatory alternatives were also using the DOT CAFE model (Method A) with a 0 - month pay back period in lieu of the 6 - month pay

back period discussed above. (A sensitivity anal ys is using Method A , discussed below, also explores longer pay back periods, as well as the combined effect of pay back period and fuel price on vehicle design decisions .) Result ant technology costs in model year 2021 results for the no - action alternative, summarized below, are quite similar to those shown above for the 6 - month pay

back period: **Table 10**  **- 14**  **MY 2021**  **Baseline Costs for HD**  **Pickups and Vans in the Flat Baseline, or Alternative 1a**

**MANUFACTURER AVERAGE**  **TECHNOLOGY COST**

**($) TOTAL COST** 

**INCREASE ($M)** Chrysler/Fiat 268 27 Daimler 0 0 Ford 248 75 General Motors 767 188 Nissan 257 3 Industry 431 292 The results below represent the impacts of other regulatory alternatives, including those defined b y the proposed standards, as incremental changes over the baseline, where the baseline is defined as the state of the world in the absence of the proposed re gulatory action. Large - scale,***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 27 macroeconomic conditions like fuel prices are constant across all alternatives, including the baseline, as are the fuel economy improvements under the no - action alternative defined b y the Phase 1 MDHD rulemaking that covers m odel ye ars 2014

– 2018 and is constant from model ye ar 2018 through 2020. In the baseline scenario, the Phase 1 standards are assumed to remain in place and at 2018 levels throughout the analy sis (i.e. MY 2030). The only difference between the definition s of the alternatives is the stringency of the proposed standards for MYs 2021

– 2025, and all of the differences in outcomes across alternatives are attributable to differences in the standards. The standards vary

in stringency

across regulatory alternat ives (1

– 5), but as discussed above, all of the standards are based on the curve developed in the Phase 1 standards that relate fuel econom y and GHG emissions to a vehicle’s work factor. The alternatives considered here represent different rates of annual increase in the curve defined for model year 2018, growing from a 0 percent annual increase (Alternative 1, the baseline or “no - action” alternative) up to a 4 percent annual increase (Alternative 5). Table 10 - 15 shows a summary of outcomes by alternative incremental to the baseline (Alternative 1 b ) for Model Year 2030 12 , with the exception of technology

penetration rates, which are absolute. The tec hnologies applied by the CAFE model have been grouped (in most cases) to give readers a general sense of which types of technology are applied more frequently than others, and are more likel y to be offered in MY2030 2b/3 vehicles. The summaries of technol ogy penetration are also intended to reflect the relationship between technology application and cost increases across the alternatives. The table rows present the degree to which specific technologies will be present in new class 2b and class 3 vehicles in 2030, and correspond to: variable valve timing (VVT) and/or variable valve lift (VVL), cylinder deactivation, direct injection, engine turbocharging, 8 - speed automatic transmissions, electric power- steering and accessory improvements, micro - hy bridizatio n (which reduces engine idle, but does not assist propulsion), full hy

bridization (integrated starter generator or strong hy brid that assists propulsion and recaptures braking energy), and aerody namic improvements to the vehicle shape. In addition to the technologies in the following tables, there are some lower - complexity technologies that have high market penetration across all the alternatives and manufacturers; low rolling - resistance tires, low friction lubricants, and reduced engine friction, for exam ple.

12 The CAFE model estimates that redesign schedules will “straddle” model year 2025, the latest year for which the agencies are proposing increases in the stringency of fuel consumption and GHG standards. Considering also that today’s analysis estimates some earning and application of “carried forward” compliance credits, the model was run extending the analysis through model year 2030.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 28 **Table 10**  **- 15**  **Summary of HD Pickup and Van Alternatives’ Impact on Industry versus the Dynamic**  **Baseline, Alternative 1b** ANNUAL STRINGENCY IN CREASE 2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y Stringency Increase Through MY 2025 2027 2025 2025 Total Stringency Increase 9.6% 16.2% 16.3% 18.5%

Average Fuel Economy (miles per gallon) Required 19.04 20.57 20.57 21.14 Ac hieved 19.14 20.61 20.83 21.27

Average Fuel Consumption (gallons /100 mi.) Required 5.25 4.86 4.86 4.73 Ac hieved 5.22 4.85 4.80 4.70

Average Greenhouse Gas Emissions (g/mi) Required 495 458 458 446 Ac hieved 491 458 453 444

Technology Penetration (%) VVT and/or VVL 46 46 46 46 Cylinder Deac. 29 21 21 21 Direct Injection 17 25 31 32 Turbocharging 55 63 63 63 8 - Speed AT 67 96 96 97 EPS, Accessories 54 80 79 79 Stop Start 0 0 10 13 Hy bridization a 0 8 35 51 Aero. Improvements 36 78 78 78 Mass Reduction (vs. No - Ac tion) CW (lb.) 239 243 325 313 CW (%) 3.7 3.7 5.0 4.8

Technology Cost (vs. No - Action) Average ($) b 578 1,348 1,655 2,080 Total ($m) c 437 1,019 1,251 1,572 Payback period (m) c 25 31 34 38 Notes: a Includes mild hybrids (ISG) and strong HEVs. b Values used in Methods A & B c Values used in Method A, calculated using a 3% discount rate . In general, the standards cause manufacturers to produce HD pickups and vans that are lighter, more aerodynamic, and more technologically complex across all the alternatives. As Table 10 - 15 shows, there is a major difference between the relativel y small increases in required fuel econom y and average incremental technology cost between the alternatives, suggesting that the challenge of improving fuel consumption and CO 2 emissions accelerates as stringency increases (i.e., that there may be a “knee” in the dependence of the challenge and on the stringency). Despite the fact that the required average fuel consumption level only changes b y about 3 percent between Alternati ve 4 and Alternative 5, average technology cost increases by***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 29 more than 25 percent. These differences help illustrate the clustered character of this market segment. The contrast between alternatives 3 and 4 is even more prominent, with a n identical required fuel econom y i mprovement leading t o price increases greater than 2 0 percent based on the more rapid rate of increase and shorter time span of Alternative 4, which achieves all of its increases by

MY 2025 while Alternative 3 continues to increase at a s lower rate until MY 2027 . Despite these differences, the increase in average payback period when moving from Alternative 3 to Alternative 4 to Alternative 5 is fairl y constant at around an additional three months for each jump in stringency . Manufacturers offer few models, ty picall y onl y a pickup truck and/or a cargo van, and while there are a large number of variants of each model, the degree of component sharing across the variants can make diversified technology application either economically impractic al or impossible. This forces manufacturers to appl y s ome technologies more broadly in order to achieve compliance than they might do in other market segments (passenger cars, for example). This difference between broad and narrow application

– where som e technologies must be applied to entire platforms, while some can be applied to individual model variants

– also explains why

certain technology

penetration rates decrease between alternatives of increasing stringency

( cylinder deactivation or mass reductions in Table 10 - 15 , for example). For those cases, narrowl y appl yi ng a more advanced (and costly) technology can be a more cost effective path to compliance and lead to reductions in the amount of lower - complexity technology

that is applied. One driver of the change in technology cost between Alternative 3 and Alternative 4 is the amount of hy

bridization resulting from the implementation of the standards. While only about 8 percent full hy

bridization (defined as either integrated starter - generator or strong h ybrid) is expected to be required to comply with Alternative 3, the higher rate of increase and compressed schedule moving from Alternative 3 and Alternative 4 is enough to increase the percentage of the fleet adopting full hy bridization to 35 percent . To the extent that manufacturers are concerned about introducing hybrid vehicles in the 2b and 3 market, it is worth noting that new vehicles s ubject to Alternative 3 achieves the same fuel econom y as new vehicles subject to Alternative 4, with less hy bridization required to achieve the improvement . The alternatives also lead to important differences in outcomes at the manufacturer level, both from the industry

average and from each other. General Motors, Ford, and Chry sler (Fiat), are expected to have approximately 95 percent of the 2b/3 new vehicle market during the ye ars that the proposed standards are in effect. Due to their importance to th is market and the similarities between their model offerings, these three manufacturers are discussed together and a summary of the way

each is impacted by

the standards appears below in Table 10 - 16 , Table 10 - 17 , and Table 10 - 18 for General Motors, Ford, and Chry sler/Fiat, respectively .***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 30 **Table 10**  **- 16**  **Summary of I**  **mpacts on General Motors by 2030 in the HD Pickup and Van Market versus the**  **Dynam ic Baseline, Alternative 1b**  ANNUAL STRINGENCY INCREASE 2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y Stringency Increase Through MY 2025 2027 2025 2025

Average Fuel Economy (miles per gallon) Required 18.38 19.96 20 20.53 Ac hieved 18.43 19.95 20.24 20.51

Average Fuel Consumption (gallons /100 mi.) Required 5.44 5.01 5 4.87 Ac hieved 5.42 5.01 4.94 4.87

Average Greenhouse Gas Emissions (g/mi) Required 507 467 467 455 Ac hieved 505 468 461 455 Technology Penetration (%) VVT and/or VVL 64 64 64 64 Cylinder Deac. 47 47 47 47 Direct Injection 18 18 36 36 Turbocharging 53 53 53 53 8 - Speed AT 36 100 100 100 EPS, Accessories 100 100 100 100 Stop Start 0 0 2 0 Hy bridization c 0 19 79 100 Aero. Improvements 100 100 100 100 Mass Reduction (vs. No - Ac tion) CW (lb.) 325 161 158 164 CW (%) 5.3 2.6 2.6 2.7

Technology Cost (vs. No - Action) Average ($) a 785 1,706 2,244 2,736 Total ($m, undiscounted) b 214 465 611 746 Notes: a Values used in Methods A & B b Values used in Method A , calculated at a 3% discount rate c Includes mild hybrids (ISG) and strong HEVs.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 31 **Table 10**  **- 17**  **Summary of Impacts**  **on Ford by 2030 in the HD Pickup and Van Market versus the Dynamic**  **Baseline, Alternative 1b** ANNUAL STRINGENCY INCREASE 2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y Stringency Increase Through MY 2025 2027 2025 2025

Average Fuel Economy (miles per gallon) Required 19.42 20.96 20.92 21.51 Ac hieved 19.5 21.04 21.28 21.8

Average Fuel Consumption (gallons /100 mi.) Required 5.15 4.77 4.78 4.65 Ac hieved 5.13 4.75 4.70 4.59

Average Greenhouse Gas Emissions (g/mi) Required 485 449 450 438 Ac hieved 482 447 443 433 Technology Penetration (%) VVT and/or VVL 34 34 34 34 Cylinder Deac. 18 0 0 0 Direct Injection 16 34 34 34 Turbocharging 51 69 69 69 8 - Speed AT 100 100 100 100 EPS, Accessories 41 62 59 59 Stop Start 0 0 20 29 Hy bridization c 0 2 14 30 Aero. Improvements 0 59 59 59 Mass Reduction (vs. No - Ac tion) CW (lb.) 210 202 379 356 CW (%) 3.2 3 5.7 5.3

Technology Cost (vs. No - Action) Average ($) a 506 1,110 1,353 1,801 Total ($m, undiscounted) b 170 372 454 604 Notes: a Values used in Methods A & B b Values used in Method A , calculated at a 3% discount rate c Includes mild hybrids (ISG) and strong HEVs.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 32 **Table 10**  **- 18**  **Summary of Impacts**  **on Fiat/Chrysler by 2030 in the HD Pickup and Van Market versus the**  **Dynam ic Baseline, Alternative 1b**  ANNUAL STRINGENCY INCREASE 2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y Stringency Increase Through MY 2025 2027 2025 2025 Average Fuel Economy (miles per gallon) Required 18.73 20.08 20.12 20.70 Ac hieved 18.83 20.06 20.10 20.70

Average Fuel Consumption (gallons /100 mi.) Required 5.34 4.98 4.97 4.83 Ac hieved 5.31 4.99 4.97 4.83 Average Greenhouse Gas Emissions (g/mi) Required 515 480 479 466 Ac hieved 512 481 480 467 Technology Penetration (%) VVT and/or VVL 40 40 40 40 Cylinder Deac. 23 23 23 23 Direct Injection 17 17 17 17 Turbocharging 74 74 74 74 8 - Speed AT 65 88 88 88 EPS, Accessories 0 100 100 100 Stop - Start 0 0 0 0 Hy bridization c 0 3 3 10 Aero. Improvements 0 100 100 100 Mass Reduction (vs. No - Ac tion) CW (lb.) 196 649 648 617 CW (%) 2.8 9.1 9.1 8.7 Technology Cost (vs. No - Action) Average ($) a 434 1,469 1,486 1,700 Total ($m, undiscounted) b 48 163 164 188 Notes: a Values used in Methods A & B b Values used in Method A , calculated at a 3% discount rate c Includes mild hybrids (ISG) and strong HEVs. The fuel consumption and GHG standards require manufacturers to achieve an average level of compliance, represented b y a sales - weighted average across the specific targets of all vehicles offered for sale in a given model year, such that each manufacturer will have a unique***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 33 required consumption/emissions level determined by the composition of its fleet, as illustrated above . However, there are more interesting differences than the small differences in required fuel econom y levels among manufacturers. In p articular, the average incremental technology cost increases with the stringency of the alternative for each manufacturer, but the size of the cost increase from one alternative to the next varies widely among them, with General Motors in particular showin g considerabl y larger increases in cost than the other two manufacturers moving both from Alternative 2 to Alternative 3, and again moving from Alternative 3 to Alternative 4. The simulation results show all three manufacturers facing large cost increase s when the proposed standards move from 2.5 percent annual increases over the period from MY 2021

– 2027 to 3.5 percent annual increases from MY 2021 - 2025 , but General Motors has the largest at 7 5 percent more than the industry average price increase for Alternative 4. GM also faces higher cost increases in Alternative 2, 2 about 50 percent more than either Ford or Fiat/Chry sler . And for the most stringent alternative considered, General Motors would face average cost increases of more than $ 2,700 , in ad dition to the more than $700 increase in the baseline

– approaching nearl y $ 3,500 per vehicle over today ’s prices. Technology

choices also differ by

manufacturer, and some of those decisions are directly responsible for the largest cost discrepancies. For example, GM is estimated to engage in the least amount of mass reduction among the Big 3 after Phase 1, and much less than Chry sler/Fiat, but reduces average vehicle mass b y over 300 pounds in the baseline

– suggesting that some of GM’s easiest Phase 1 co mpliance opportunities can be found in lightweighting technologies. Similarly , Chry sler/Fiat applies less hy bridization than the others, and much less than General Motors, which is simulated to have h ybrids (either integrated starter generator or full hy b rid sy stem) on much of its fleet by 2030, nearly

20 percent of which will be strong hy brids, in Alternative 4 and the strong hybrid share decreases to about 18 percent in Alternative 5 , as some lower level technologies are applied more broadly . Because the anal ys is applies the same technology

inputs and the same logic for selecting among available opportunities to apply technology , the unique situation of each manufacturer determined which technology path was the most cost - effective. In order to under stand the differences in incremental technology costs and fuel econom y achievement across manufacturers in this market segment, it is important to understand the differences in their starting position relative to the proposed standards. One important fact or, made more obvious in the following figures, is the difference between the fuel economy and performance of the recently redesigned vans offered by Fiat/Chry sler and Ford (the Promaster and Transit, respectivel y) , and the more traditionally- styl ed vans that continue to be offered b y General Motors (the Express/Savannah). In MY 2014, Ford began the phase - out of the Econoline van platform, moving those volumes to the Euro - styl e Transit vans (discussed in more detail in Section 10.1.2 ). The Transit platform represents a significant improvement over the existing Econoline platform from the perspective of fuel economy , and for the purpose of complying with the standards, the relationship between the Transit’s work factor and fuel econom y is a more favorable one than the Econoline vans it replaces. Since the redesign of van offerings from both Chrysler/Fiat and Ford occur in (or prior to) the 2014 model year, the costs , fuel consumption improvements, and reductions of vehicle mass associated with those redesigns are included in the anal ys is fleet, meaning they are not carried as part of the compliance modeling exercise. By

contrast, General Motors is simulated to redes ign their van offerings after***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 34 2014, such that there is a greater potential for these vehicles to incur additional costs attributable to new standards, unlike the costs associated with the recent redesigns of their competitors. The inclusion of these new F ord and Chry sler/Fiat products in the anal ys is fleet is the primary driver of the cost discrepancy

between GM and its competitors in both the baseline and Alternative 2, when Ford and Chry sler/Fiat have to apply considerabl y less technology to achieve comp liance. Figure 10 - 3 and Figure 10 - 4 show th e relationship between work factor and fuel econom y for the model variants offered b y GM, Ford and Chry sler/Fiat for gasoline ( Figure 10 - 3 **Error! Reference source not found.** ) and diesel ( Figure 10 - 4 ) vehicles based on product information the manufacturers supplied to the agencies. In the figures, vans are represented b y crosses and pickup trucks by circles, with a different color corresponding to each member of the Big 3 (blue, green, and orange for Fiat/Chry sler, Ford, and General Motors, respectivel y). In Figure 10 - 3 , the field of green crosses in the upper left shows the impact of the Transit vans on Ford’s product mix. While Chrysler/Fiat has a single van below that cloud, the gasoline powered Promaster, both have real separation from GM’s van offerings which are generally higher in work factor and considerabl y lower in fuel econom y. Ford has a cluster of gasoline pickup truck variants with among the highest work factors and lowest fuel economies, but another cluster of pickup trucks with work factors between 4500 and 6000 and higher fuel econom y values than nearly all competitors’ pickup trucks in that range. The curves provide a sliding a scale of fuel economy targets for vehicle models based on work factor, but the changes in scale are not sufficient to overcome GM’s poor starting position in fuel econom y relative to its peers. As Figure 10 - 4 shows, this pattern is even stronger in the diesel market, where both GM and Chrysler/Fiat have multiple offerings above 20 MPG. These high MPG models are all vans and, as noted above, GM’s van offerings would need changes in order to provide fuel economy competing with offerings of GM’s peers

– even in the absence of regulatory

pressure.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 35 **Figure 10**  **- 3**  **Com parison of Fuel Economy and Work Factor for Gasoline Vehicles in the Analysis Fleet** 

**2000 2500**  **3000 3500**  **4000 4500**  **5000 5500**  **6000 6500**  **7000 7500**  **8000 8500**  **Work Factor 10**  **11 12**  **13 14**  **15 16**  **17 18**  **19 20**  **21 22** **F u** **e l** **E c** **o n** **o m** **y o** **n G** **a s** **o l** **i n** **e**

**Manufacturer**

**Fiat Ford**  **General Motors Bosdy Style**  **Pickup Van** ***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 36 **Figure 10**  **- 4**  **Com parison of Fuel Economy and Work Factor for Diesel Vehicles in the Analysis Fleet**  In the context of an averaging such as under the fuel consumption and GHG standards, individual model offerings mean little without cons idering the sales volumes associated with those models. Figure 10 - 5 shows two pictures of empirical cumulative distributions, curves that show the sales weighted mix of work factor (on the left) and fuel economy (on the right) increasing from zero percent of sales to 100 percent of sales. At any given point in the curve, the value on the (vertical) y- axis shows the percentage of total sales that are l ess than or equal to the work factor (or fuel economy) at that point. **1500 2000**  **2500 3000**  **3500 4000**  **4500 5000**  **5500 6000**  **6500 7000**  **7500 8000**  **8500 9000**  **9500 10000**  **10500 Work Factor**  **11 12**  **13 14**  **15 16**  **17 18**  **19 20**  **21 22**  **23 24**  **25 26**  **27 28** **F u** **e l** **E c** **o n** **o m** **y o** **n D** **i e** **s e** **l**

**Manufacturer**

**Fiat Ford**  **General Motors Bosdy Style**  **Pickup Van** ***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 37 **Figure 10**  **- 5**  **Com paring sales mix for Chrysler/Fiat, GM, and Ford**  Since larger work factors correspond to lower fuel econom y targets, the standards provide an incentive for a manufacturer to have more of its production closer to the right side of the work factor graph to reduce its required average fuel economy level (i.e., under the standards, to increase its required average fuel consumption and GHG levels), although this incentive is offset b y the tendency of fuel consumption to increase as vehicle pay load and towing capacity increase, and when 4WD is added . Figure 10 - 5 shows Chry sler/Fiat with the most favorable position, from the perspective of work factor, followed b y GM. Although Ford’s sales mix of work factors is the least favorable, Ford’s sales mix generall y has the highes t fuel econom y of the Big 3. As the graph on the right in Figure 10 - 5 shows , Ford has the most favorable sales mix of high fuel econom y vehicles, and generall y outperforms both other manufacturers at each fuel econom y level. The sales distribution of fuel economies highlights the other important reason that GM appears to face much higher costs than their competitors in this segment: not only does GM h ave consistentl y lower fuel econom y than Ford and Chry sler in their MY2014 vehicle fleet, they also have no models (at any significant level of sales) achieving more than 18 MPG, while both Ford and Chry sler/Fiat have about 40 percent and 10 percent of sal es, respectivel y, at higher levels of fuel econom y. Some of this discrepancy could be explained by measurement error in the pre - model - ye ar compliance data; GM’s final submission for model year 2014 may contain higher fuel economies as a result of more dir ect vehicle testing, or a different mix of final sales volumes that makes their starting position more favorable. The agencies intend to update the data in the anal ys is fleet before issuing the final rule. The remaining 5 percent of the 2b/3 market is attributed to two manufacturers, Daimler and Nissan, which, unlike the other manufacturers in this market segment, only produce vans.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 38 The vans offered b y both manufacturers currentl y utilize two engines and two transmissions, although both Nissan engines are gasoline engines and both Daimler engines are diesels. Despite the logical grouping, these two manufacturers are impacted much differently by the proposed standards. For the least stringent alternative considered, Daimler adds no technology and incurs no incremental cost in order to comply with the standards. At stringency increases greater than or equal to 3.5 percent per year, Daimler onl y really improves some of their transmissions of its Sprinter vans. By

contrast, Nissan’s starting position is mu ch weaker and their compliance costs closer to the industry

average in Table 10 - 15 . This difference could increase if the anal ys is fleet supporting the final rule includes forthcoming Nissan HD pickups.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 39 **Table 10**  **- 19**  **Summary of Impacts**  **on Daimler by 2030 in the HD Pickup and Van Market versus the**  **Dynam ic Baseline, Alternative 1b**  ANNUAL STRINGENCY INCREASE 2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y Stringency Increase Through MY 2025 2027 2025 2025 Average Fuel Economy (miles per gallon) Required 23.36 25.19 25.25 25.91 Ac hieved 25.23 25.79 25.79 26.53

Average Fuel Consumption (gallons /100 mi.) Required 4.28 3.97 3.96 3.86 Ac hieved 3.96 3.88 3.88 3.77 Average Greenhouse Gas Emissions (g/mi) Required 436 404 404 393 Ac hieved 404 395 395 384 Technology Penetration (%) VVT and/or VVL 0 0 0 0 Cylinder Deac. 0 0 0 0 Direct Injection 0 0 0 0 Turbocharging 44 44 44 44 8 - Speed AT 0 44 44 100 EPS, Accessories 0 0 0 0 Stop - Start 0 0 0 0 Hy bridization c 0 0 0 0 Aero. Improvements 0 0 0 0 Mass Reduction (vs. No - Ac tion) CW (lb.) 0 0 0 0 CW (%) 0 0 0 0 Technology Cost (vs. No - Action) Average ($) a 0 165 165 374 Total ($m, undiscounted) b 0 4 4 9 Notes: a Values used in Methods A & B b Values used in Method A , calculated at a 3% discount rate c Includes mild hybrids (ISG) and strong HEVs.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 40 **Table 10**  **- 20**  **Summary of Impacts**  **on Nissan by 2030 in the HD Pickup and Van Market versus**  **the Dynamic**  **Baseline, Alternative 1b** ANNUAL STRINGENCY INCREASE 2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y Stringency Increase Through MY 2025 2027 2025 2025 Average Fuel Economy (miles per gallon) Required 19.64 21.19 20.92 21.46 Ac hieved 19.84 21.17 21.19 21.51 Average Fuel Consumption (gallons /100 mi.) Required 5.09 44.72 4.78 4.66 Ac hieved 5.04 4.72 4.72 4.65 Average Greenhouse Gas Emissions (g/mi) Required 452 419 425 414 Ac hieved 448 419 419 413 Technology Penetration (%) VVT and/or VVL 100 100 100 100 Cylinder Deac. 49 49 49 49 Direct Injection 51 51 51 100 Turbocharging 51 51 51 50 8 - Speed AT 0 51 51 51 EPS, Accessories 0 100 100 100 Stop - Start 0 0 0 0 Hy bridization c 0 0 0 28 Aero. Improvements 0 100 100 100 Mass Reduction (vs. No - Ac tion) CW (lb.) 0 0 307 303 CW (%) 0 0 5 4.9 Technology Cost (vs. No - Action) Average ($) a 378 1,150 1,347 1,935 Total ($m, undiscounted) b 5 15.1 17.7 25.4 Notes: a Values used in Methods A & B b Values used in Method A , calculated at a 3% discount rate c Includes mild hybrids (ISG) and strong HEVs.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 41 As Table 10 - 19 and Table 10 - 20 show, Nissan applies more technology than Daimler in the less stringent alternatives and significantly more technology

with increasing stringency . The Euro - style Sprinter vans that comprise all of Daimler’s model offerings in this segment put Daimler in a favorable position. However, those vans are already advanced

– cont aining downsized diesel engines and advanced aerodynamic profiles. Much like the Ford Transit vans, the recent improvements to the Sprinter vans occurred outside the scope of the compliance modeling so the costs of the improvements are not captured in the anal ys is. Although Daimler’s required fuel econom y level is much higher than Nissan’s (in miles per gallon), Nissan starts from a much weaker position than Daimler and must incorporate additional engine, transmission, platform - level technologies (e.g. mas s reduction and aerodynamic improvements) in order to achieve compliance. In fact, more than 25 percent of Nissan’s van offerings become are projected to contain integrated starter generators by 2030 in Alternative 5. 

### 10.2.2.2 **Estimated Consumer Impacts**  The cons umer impacts of the rule are more straightforward. Table 10 - 21 shows the impact on the average consumer who buys a new class 2 b or 3 vehicle in model ye ar 2030. All dollar values are discounted at a rate of 7 percent per year from the time of purchase (except the average price increase, which occurs at the time of purchase). The additional costs associated with increases in taxes, registration fees, and financing costs are also captured in the table .***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 42 **Table 10**  **- 21**  **Summary of Individual Consumer Impacts**  **in MY 2030 in the HD Pickup and Van2b3 Market** 

**Segm ent using Method A and versus the Dynamic Baseline, Alternative 1b**  a ANNUAL STRINGENCY INCREASE INCREASES 2.0%/Y 2.5%/Y 3.5%/Y 4.0%/Y Stringency Increase Through MY 2025 2027 2025 2025

Value of Lifetime Fuel Savings (discounted 2012 dollars) Pretax 2,068 3,924 4,180 4,676 Tax 210 409 438 491 Total 2,278 4,334 4,618 5,168

Economic Benefits (discounted 2012 dollars) Mobility Benefit 244 437 472 525 Avoided Refueling Time 86 164 172 193

New Vehicle Purchase (vs. No - Action Alternative) Avg. Cost Increase ($) 578 1,348 1,655 2,080 Avg. Payback (years) 2.5 3 3.4 3.9 Additional costs ($) 120 280 344 432

Net Lifetime Consumer Benefits (discounted $) Total Net Benefits 1,910 3,307 3,263 3,374 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 As expected, a consumer’s lifetime fuel savings increase monotonicall y across the alternatives. The mobility benefit in Table 10 - 21 refers to the value of additional miles that an individual consumer travels as a result of reduced per - mile travel costs. The additional miles result in additional fuel consumption and represent foregone fuel savings, but are valued b y consumers at the cost of the additio nal fuel plus the consumer surplus (a measure of the increase in welfare that consumers achieve b y having more mobility ). The refueling benefit measures the value of time saved through reduced refueling events, the result of improved fuel economy and rang e in vehicles that have been modified in response to the standards. There are some limitations to using pay back period as a measure, as it accounts for fuel expenditures and incremental costs associated with taxes, registration fees and financing, and in creased maintenance costs, but not the cost of potential repairs or replacements, which may or may not be more expensive with more advanced technology .***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 43 Overall, the average consumer is likely to see discounted lifetime benefits that are multiples of the price increases faced when purchasing the new vehicle in MY 2030. In particular, the net present value of future benefits at the time of purchase are estimated to be 3.5, 3.0, 2.2, and 1.8 times the price increase of the average new MY2030 vehicle for Alternatives 2 - 5 , respectivel y. As the table above illustrates, the preferred alternative has the highest ratio of discounted future consumer benefits to consumer costs. 

### 10.2.2.3 **Social and Environmental Impacts** S ocial benefits increase with the increasing stringency of the alternatives. A s in the consumer analy sis, the net benefits continue to increase with increasing stringency

– suggesting that benefits are still increasing faster than costs for even the most s tringent alternative. **Table 10**  **- 22**  **Summary of Total Social Costs**  **and Benefits**  **Through MY2029 in the HD Pickup and Van**

**Market Segment using Method A and versus the Dynamic Baseline, Alternative 1b** a **ALTERNATI VE**  **2 3**  **4 5**  Annual Stringency Increase 2.0% 2.5% 3.5% 4.0% Stringency Increase Through MY 2025 2027 2025 2025 Fuel Purchases ($billion) Pretax Savings 9.6 15.9 19.1 22.2 Fuel Externalities ($billion) Energy Security 0.5 0.9 1.1 1.3 CO 2 emissions b 1.9 3.2 3.8 4.4

VMT - Related Externalities ($billion) Driving Surplus 1.1 1.8 2.1 2.4 Refueling Surplus 0.4 0.7 0.8 0.9 Congestion - 0.2 - 0.4 - 0.4 - 0.5 Accidents - 0.1 - 0.2 - 0.2 - 0.3 Noise 0 0 0 0 Fatalities 0.1 - 0.2 - 0.2 - 0.5 Criteria Emissions 0.6 1.1 1.3 1.6

Technology Costs vs. No - Action ($billion) Incremental Cost 2.5 5.0 7.2 9.7 Additional Costs 0.5 1.0 1.5 2.0 Benefit Cost Summary ($billion) Total Social Cost 3.3 6.8 9.5 13.0 Total Social Benefit 13.9 22.7 27.4 31.7 Net Social Benefit 10.6 15.9 17.9 18.7 * All dollar valu es are discounted at a rate of 3 percent per year from the time of purchase . Notes:***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 44 a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b Using the 3% average social cost of CO 2 value. There are four distinct social cost of CO 2 values presented in the Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis u nder Executive Order 12866 (2010 and 2013) . The CO 2 emissions presented here would be valued lower with one of those other three values and higher at the other two values. Table 10 - 22 provides a summary of benefits and costs, cumulative from MY2015

– MY20 29 (although the early years of the series have nearl y zero incremental costs and benefits over the baseline), for each alternative. In the social perspective, fuel savings are considered net of fuel taxes, which are a transfer from purchasers of fuel to so ciety at large. The energy security component represents the risk premium associated with exposure to oil price spikes and the economic consequences of adapting to them. This externality is monetized on a per - gallon basis, just as the social cost of carb on is used in this analy sis. Just as the previous two externalities are caused b y fuel consumption, others are caused b y travel itself. The additional VMT resulting from the increase in travel demand that occurs when the price of driving decreases (i.e. the rebound effect), not only

leads to increased mobility , but also to increases in congestion, noise, accidents, and per - mile emissions of criteria pollutants like carbon monoxide and diesel particulates. Although increases in VMT lead to increases in tailpipe emissions of criteria pollutants, the proposed regulations decrease overall consumption enough that the emissions reductions associated with the remainder of the fuel cycle (extraction, refining, transportation and distribution) are large enough to create a net reduction in the emissions of criteria pollutants. 13 A full presentation of the costs and benefits, and the considerations that have gone into each cost and benefit category

— such as how energy

security premiums were developed, how the social c osts of carbon and co - pollutant benefits were developed, etc.

—is presented in Section IX of the preamble and in Chapters 7 and 8 of this draft RIA for each regulated segment (engines, HD pickups and vans, vocational vehicles, tractors and trailers). Anoth er side effect of increased VMT is the likely increase in traffic fatalities, which is a function of the total vehicle travel in each ye ar. As Table 10 - 22 illustrates, the positive social cost associated with traffic fatalities is the result of an additional - 10 (implying that Alternative 2 actuall y leads to a reduction in fatalities over the baseline, due to the application of mass reduction technolo gies), 35 , 36 , and 66 fatalities for Alternatives 2 - 5, respectivel y. To put those numbers in context, the baseline contains nearl y 2 5 ,000 fatalities attributable to 2b/3 vehicles over the same period. The incremental fatalities associated with the altern atives translate to less than - 0.4, 0.1, 0.1, and 0. 3 percent increases over the MY2015 - 202 9 baseline, respectivel y. The CAFE model was used to estimate the emissions impacts of the various alternatives that are the result of lower fuel consumption, but in creased vehicle miles traveled for vehicle

13 For a more detailed discussion of the results from the CAFE Model on the proposed heavy duty pickups and vans regulation’s impact on emissions of CO 2 and criteria pollutants, see NHTSA’s accompanying Draft Environmental Im pact Statement.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

1 0 - 45 produced in model ye ars subject to the standards in the alternatives. Criteria pollutants are largel y the result of vehicle use, and accrue on a per- mile - of - travel basis, but the alternatives still generall y lead to emissions reductions. Although vehicle use increases under each of the alternatives, upstream emissions associated with fuel refining, transportation and distribution are reduced for each gallon of fuel saved and that savings is larger than the increm ental increase in emissions associated with increased travel. The net of the two factors is a savings of criteria (and other) pollutant emissions. **Table 10**  **- 23**  **Summary of Environmental Impacts Through MY2029 in the HD Pickup and Van Market** 

**Segm ent, using Method A and versus the Dynamic Baseline, Alternative 1b**  a ANNUAL STRINGENCY INCREASE 2.0% 2.5% 3.5% 4.0% Stringency Increase Through MY 2025 2027 2025 2025 Greenhouse Gas Emissions vs. No - Action Alternative CO 2 (MMT) 54 91 110 127 CH 4 and N2O (tons) 65,600 111,400 133,700 155,300 Other Emissions vs. No - Action Alternative (tons) CO 10,400 20,700 25,800 30,400 VOC and NO X 23,800 43,600 53,500 62,200 PM 1,470 2,550 3,090 3,590 SO 2 11,400 19,900 24,100 28,000 Air Toxics 44 47 49 55 Diesel PM10 2,470 4,350 5,300 6,160 Other Emissions vs. No - Action Alternative (% reduction) CO 0.1 0.3 0.4 0.4 VOC and NO X 1.1 2.1 2.6 3.0 PM 1.7 3.0 3.6 4.2 SO 2 2.9 5.1 6.2 7.2 Air Toxics 0.1 0.1 0.1 0.2 Diesel PM10 2.7 4.8 5.9 6.8 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 In addition to comparing environmental impacts of the alternatives against a dy namic baseline that shows some improvement over time, compared to today ’s fleet, even in the absence of the alternatives, the environmental impacts from the Method A anal ys is were compared against a flat baseline. This other comparison is summarized below, but both comparisons are discussed in greater detail in the Draft EI S.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 46 **Table 10**  **- 24**  **Summary of Environmental Impacts Through MY2029 in the HD Pickup and Van Market**

**Segm ent, using Method A and versus the Flat Baseline, Alternative 1a**  a ANNUAL STRINGENCY INCREASE 2.0% 2.5% 3.5% 4.0% Stringency Increase Through MY 2025 2027 2025 2025 Greenhouse Gas Emissions vs. No - Action Alternative CO 2 (MMT) 66 105 127 142 CH 4 and N2O (tons) 79,700 127,400 154,800 172,800 Other Emissions vs. No - Action Alternative (tons) CO 11,630 22,160 28,030 32,370 VOC and NO X 28,280 48,770 60,180 68,050 PM 1,780 2,900 3,550 3,980 SO2 13,780 22,580 27,660 31,020 Air Toxics 60 65 72 73 Diesel PM10 2,980 4,930 6,060 6,810 Other Emissions vs. No - Action Alternative (% reduction) CO 0.2 0.3 0.4 0.4 VOC and NO X 1.4 2.3 2.9 3.3 PM 2.1 3.4 4.2 4.7 SO2 3.5 5.7 7.0 7.9 Air Toxics 0.2 0.2 0.2 0.2 Diesel PM10 3.3 5.4 6.7 7.5 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 

### 10.2.2.4 **Sensitivity Analysis to Different Inputs to the CAFE Model** This section describes some of the principal sensitivity results, obtained by

running the various scenarios describing the policy alternatives with alternative inputs. OMB Circular A - 4 indicates that “it is usually necessary to provide a sensitivity

anal y sis to reveal whether, and to what extent, the results of the anal ys is are sensitive to plausible changes in the main assumptions and numeric inputs.” 14 Considering this guidance, a number of sensitivity analy ses were performed using anal ys is Method A to e xamine important assumptions and inputs, including the following: 1. Pay back Period : In addition to the 0 and 6 month pay back periods discussed above, also evaluated cases involving pay back periods of 12, 18, and 24 months. 2. Fuel Prices : Evaluated cases invo lving fuel prices from the AEO 2014 low and high oil price scenarios. (See AEO - Low and AEO - High in the tables.) 3. Fuel Prices and Pay

back Period : Evaluated one side case involving a 0 month pay back period combined with fuel prices from the AEO 2014 low oil price scenario, and one side case with a 24 month payback period combined with fuel prices from the AEO 2014 high oil price scenario.

14 Available at http://www.whitehouse.gov/omb/circulars_a004_a - 4/ .***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 47 4. Benefits to Vehicle Buye rs : The main Method A anal ys is assumes there is no loss in value to owner/operators resulting fro m vehicles that have an increase in price and higher fuel econom y. NHTSA performed this sensitivity anal ys is assuming that there is a 25, or 50 percent loss in value to owner/operators

– equivalent to the assumption that owner/operators will only value th e calculated benefits they will achieve at 75, or 50 percent, respectively , of the main anal ys is estimates. (These are labeled as 75pct Owner/operatorBenefit and 50pct Owner/operatorBenefit .) 5. Value of Avoided GHG Emissions : Evaluated side cases involving lo wer and higher valuation of avoided CO 2 emissions, expressed as the social cost of carbon (SCC). 6. Rebound Effect : Evaluated side cases involving rebound effect values of 5 percent, 15 percent, and 20 percent. (These are labeled as 05PctReboundEffect, 15P ctReboundEffect and 20PctReboundEffect .) 7. RPE - based Markup : Evaluated a side case using a retail price equivalent (RPE) markup factor of 1.5 for non - electrification technologies, which is consistent with the NAS estimation for technologies manufactured b y suppliers, and a RPE markup factor of 1.33 for electrification technologies (mild and strong HEV). 8. ICM - based Post - Warranty Repair Costs : NHTSA evaluated a side case that scaled the frequency of repair b y vehicle survival rates, assumes that per- vehicle repair costs during the post - warranty period are the same as in the in - warranty period, and that repair costs are proportional to incremental direct costs (therefore vehicles with additional components will have increased repair costs). 9. Mass - Safety Effect : Evaluated side cases with the mass - safety impact coefficient at the values defining the 5 th and 95 th percent points of the confidence interval estimated in the underlying statistical analy sis. (These are labeled MassFatalityC oeff05pct and MassFatalityC oef

f95pct.) 10. Strong HEVs : Evaluated a side case in which strong HEVs were excluded from the set of technology

estimated to be available for HD pickups and vans through model year 2030. An additional “no strong HEV” case was run where all GM gasoline - engine vans were allowed to have turbo - downsized engines to provide a lower - cost option for compliance. These cases were all run for both 0 - month and 6 - month pay back periods. 11. Diesel Downsizing : Evaluated a side case in which downsizing of diesel engines was esti mated to be more widely available to HD pickups and vans. 12. Technology

Effectiveness : Evaluated side cases involving inputs reflecting lower and higher impacts of technologies on fuel consumption. 13. Technology

Direct Costs : Evaluated side cases involving inp uts reflecting lower and higher direct incremental costs for fuel - saving technologies. 14. Fleet Mix : Evaluated a side case in which the shares of individual vehicle models and configurations were kept constant at estimated current levels. Table 10 - 25 below summarize s key metrics for each of the cases included in the sensitivity

anal ys is using Method A for the proposed alternative . The table reflects the percent change in the metrics (columns) , relative to the main anal ys is, the proposed alternative 3. For each sensitivity

run, the change in the metric can we described as the difference between the baseline and the preferred alternative for the sensitivity case, minus the difference between the preferred alternative and the baseline in the main analy sis, divided by the difference between the preferred alternative and the baseline in the main analy sis. Or,***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 48 = ∆ − ∆

∆ ∙ 100 Each metric represents the sum of the impact of the preferred alternative over the model ye ars 2018

– 2029, and the percent changes in the table represent percent changes to those sums. **Table 10**  **- 25**  **Sensitivity Analysis Results from CAFE Model**  **for the Proposed Standards in the HD Pickup**  **and Van Market Segm ent using Method A and versus the Dynam** **ic Baseline, Alternative 1b (Cells are**  **p ercent**  **c hange f** 

**rom b**  **ase c**  **ase )**  **a** Sensitivity Case Fuel

Savings (gallons) CO2

savings (MMT) Fuel Savings

($) Social

Costs Social

Benefits Social

Net Benefits 0 Month Payback 14.0% 14.5% 15.1% 5.6% 15.1% 18.2% 12 Month Payback - 4.8% - 4.7% - 4.5% - 2.5% - 4.7% - 5.4% 18 Month Payback - 29.2% - 28.1% - 26.5% - 14.1% - 26.8% - 31.1% 24 Month Payback - 42.9% - 42.4% - 41.9% - 23.2% - 42.1% - 48.4% AEO - Lo w 3.3% 3.5% - 27.9% - 10.8% - 22.2% - 26.1% AEO - High - 7.0% - 7.2% 23.3% 1.4% 19.5% 25.6% AEO - Lo w, 0 Month Payback 18.6% 19.3% - 16.5% - 3.4% - 10.1% - 12.3% AEO - High, 24 Month Payback - 63.8% - 64.6% - 54.4% - 49.9% - 55.7% - 57.7% 50pct Owner/operator Benefit 0.0% 0.0% - 50.0% 0.0% - 34.6% - 46.2% 75pct Owner/operator Benefit 0.0% 0.0% - 25.0% 0.0% - 17.3% - 23.1% Lo w SCC 0.0% 0.0% 0.0% 0.0% - 10.6% - 14.1% Lo w SCC, 0 Month Payback 14.0% 14.5% 15.1% 5.6% 2.9% 2.0% High SCC 0.0% 0.0% 0.0% 0.0% 7.8% 10.4% High SCC, 0 Month Payback 14.0% 14.5% 15.1% 5.6% 24.0% 30.1% Very High SCC 0.0% 0.0% 0.0% 0.0% 28.7% 38.4% Very High SCC, 0 Month Payback 14.0% 14.5% 15.1% 5.6% 48.0% 62.2% 05 Pct Rebound Effect 4.6% 4.6% 4.6% - 12.9% 0.4% 4.8% 15 Pct Rebound Effect - 4.6% - 4.6% - 4.6% 12.9% - 0.4% - 4.8% 20 Pct Rebound Effect - 9.1% - 9.2% - 9.2% 25.7% - 0.8% - 9.7% RPE - Based Markup - 3.2% - 1.5% 0.3% 31.4% - 0.1% - 10.6% Mass Fatality Coeff 05pct 0.0% 0.0% 0.0% - 23 . 6 % 0.0% 7.9 % Mass Fatality Coeff 95pct 0.0% 0.0% 0.0% 23.9 % 0.0% - 8 .0% NoSHEVs, - 6.9% - 6.2% - 5.3% 19.2% - 5.4% - 13.7%***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 49 NoSHEVs, 0 Month Payback 7.7% 9.1% 10.7% 29.0% 10.5% 4.3% NoSHEVs, GM Turbo Vans - 6.7% - 5.8% - 5.0% 2.3% - 5.1% - 7.6% NoSHEVs, GM Turbo Vans, 0 Month Pay

back 8.2% 9.8% 11.5% - 1.2% 11.3% 15.4% Lo wer Effectiveness - 7.8% - 7.8% - 8.1% 39.5% - 8.0% - 23.9% Higher Effectiveness - 10.6% - 10.3% - 10.0% - 23.3% - 10.2% - 5.8% Lo wer Direct Costs 0.9% 2.7% 4.8% 18.4% 4.3% - 0.4% Higher Direct Costs - 4.1% - 3.8% - 3.5% 75.3% - 3.8% - 30.3% Wider Diesel Downsizing - 1.5% - 1.0% - 0.6% - 10.3% - 0.8% 2.4% 07 Pct Discount Rate 0.0% 0.0% - 100.0% - 41.7% - 100.0% - 119.5% 07 Pct DR, 0 Month Payback 14.0% 14.5% - 37.9% - 30.7% - 30.7% - 30.7% Allow Gas To Diesel 15.5% 5.3% - 100.0% 16.8% - 100.0% - 139.1% Allow Gas To Diesel, 0 Month Payback 32.1% 22.6% 14.5% 46.8% 17.0% 7.0% flat mix after 2016 1.1% 0.9% 0.7% 2.6% 0.8% 0.2% Note: a For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Section X.A.1. For some of the cases for which results are presented above, the sensitivity of results to changes in inputs is simple, direct, and easil y observed. For example, changes to valuation of avoided GHG emissions impact only this portion of the estimated economic benefits; manufacturers’ responses and corresponding costs are not impacted. Similarly , a higher discount rate does not affect phys ical quantities saved (gallons of fuel and metric tons of CO2 in the table), but reduces the value of the costs and benefits attributable to the proposed standards in an intuitive way . Some other cases warrant closer consideration: First, cases involving alternatives to the reference six - month pay back period involve different degrees of fuel consumption improvement, and these differences are greatest in the no action alternative defining the baseline. Because all estimated impacts of the proposed standards are shown as incremental values relative to this baseline, longer pay back periods correspond to smaller estimates of incremental impacts , as fuel econom y increasingly improves in the absence of the rule and manufacturers are compe lled to add less technology in order to comply

with the standards . Second, cases involving different fuel prices similarly involve different degrees of fuel econom y improvement in the absence of the standard, as more, or less, improvement occurs as a resul t of more, or fewer, technologies appearing cost effective to owner/operators . Lower fuel prices correspond to increases in fuel savings on a volumetric basis, as the standard is responsible for a greater amount of the fuel economy improvement, but the value of fuel savings decreases because each gallon saved is worth less when fuel prices are low. Higher fuel prices correspond to reductions in the volumetric fuel savings attributable to the proposed standards,***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 50 but lead to increases in the value of fuel saved because each gallon saved is worth more when fuel prices are high. Third, because the pay

back period and fuel price inputs work in opposing directions, the relative magnitude of each is important to consider for the combined sensitivity cases. While th e low price and 0 - month pay back case leads to significant volumetric savings compared to the main anal ys is, the low fuel price is still sufficient to produce a negative change in net benefits. Similarly , the high price and 24 - month pay back case results in large reductions to volumetric savings that can be attributed to the proposed standards, but the presence of high fuel prices is not sufficient to lead to increases in either the dollar value of fuel savings or net social benefits. Fourth, the cases involv ing different inputs defining the availability of some technologies do not impact equally

the estimated impacts across all manufacturers. Section VI.C.8 of the Preamble provides a discussion of a sensitivity analy sis that excludes strong hy brids and inclu des the use of downsized turbocharged engine s in vans currentl y equipped with large V - 8 engines. The modeling results for this analy sis are provided in Section IV . C.8 and in the table above. The no strong h ybrid anal ys is shows that GM could comply with the proposed preferred Alternative 3 without strong h ybrids based on the use of turbo downsizing on all of their HD gasoline vans. Alternativel y , when the anal ys is is modified to allow for wider application of diesel engines, strong HEV application for GM drops slightl y (from 19 percent to 17 percent ) in MY2030, average per - vehicle costs drop slightl y (by about $50), but MY2030 additional penetration rates of diesel engines increase b y about 10 percent . Manufacturer - specific model results accompanyi ng today’ s rule show the extent to which individual manufacturers’ potential responses to the standards vary with these alternative assumptions regarding the availability and applicability

of fuel - saving technologies. However, across all of these sensitivity case s, the model projects social costs increase (as a result of increases in technology costs) when manufacturers choose to comply with the proposed regulations without the use of strong hy brids. Fifth, the cases that vary

the effectiveness and direct cost of available technologies produce nuanced results in the context of even the 0 - month pay back case. In the case of effectiveness changes, both sensitivity cases result in reductions to the volumetric fuel savings attributable to the proposal; lower effectivene ss because the technologies applied in response to the standards save less fuel, and higher effectiveness because more of the increase in fuel econom y occurs in the baseline. However, for both cases, social costs (a strong prox y for technology

costs) move in the intuitive direction. The cases that vary

direct costs show volumetric fuel savings increasing under lower direct technology

costs despite additional fuel economy improvements in the baseline, as more aggressive technology

becomes cost effective. Hi gher direct costs lead to decreases in volumetric fuel savings, as more of the fuel economy improvement can be attributed to the rule. In both cases, social costs (as a result of technology costs) move in the intuitive direction. If instead, the main anal ysis had used the same assumptions as the sensitivity cases described above, the impacts of the proposed standards for HD Pickups and Vans would be as described in Table 10 - 26 .***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 51 **Table 10**  **- 26**  **: Costs**  **and Benefits of Proposed Standards for HD Pickups and Vans Under Alternative Assu**  **mptions** Sensitivity Case Fuel Savings (billion gallons) CO2

Reduction (MM T) Fuel

Savings ($billion) Social

Costs ($billion) Social

Benefits ($billion) Net

Social Benefits ($billion) 6 Month Payback (main) 7.8 94.1 15.9 5.5 23.5 18.0 0 Month Payback 8.9 107.7 18.3 5.8 27.0 21.3 12 Month Payback 7.4 87.2 15.2 5.6 21.9 16.3 18 Month Payback 5.5 65.8 11.7 4.9 16.8 11.9 24 Month Payback 4.5 52.7 9.2 4.4 13.3 8.9 AEO - Lo w 8.1 94.7 11.5 5.1 17.8 12.7 AEO - High 7.3 84.9 19.6 5.8 27.4 21.6 AEO - Lo w, 0 Month Payback 9.3 109.1 13.3 5.6 20.6 15.1 AEO - High, 24 Month Payback 2.8 32.4 7.2 2.9 10.2 7.3 50pct Owner/operator Benefit 7.8 91.5 8.0 5.8 15.0 9.2 75pct Owner/operator Benefit 7.8 91.5 11.9 5.8 19.0 13.2 Lo w SCC 7.8 91.5 15.9 5.8 20.5 14.8 Lo w SCC, 0 Month Payback 8.9 104.7 18.3 6.1 23.6 17.5 High SCC 7.8 91.5 15.9 5.8 24.7 19.0 High SCC, 0 Month Payback 8.9 104.7 18.3 6.1 28.5 22.4 Very High SCC 7.8 91.5 15.9 5.8 29.5 23.8 Very High SCC, 0 Month Payback 8.9 104.7 18.3 6.1 34.0 27.9 05 Pct Rebound Effect 8.2 95.7 16.6 5.0 23.0 18.0 15 Pct Rebound Effect 7.5 87.2 15.2 6.5 22.9 16.4 20 Pct Rebound Effect 7.1 83.0 14.4 7.2 22.8 15.5 RPE - Based Markup 7.6 90.1 16.0 7.6 22.9 15.4 Mass Fatality Coeff 05pct 7.8 91.5 15.9 4.4 23.0 18.5 Mass Fatality Coeff 95pct 7.8 91.5 15.9 7.1 23.0 15.8 NoSHEVs 7.2 84.3 14.6 8.0 21.1 13.1 NoSHEVs, 0 Month Payback 7.0 82.0 14.3 4.4 20.6 16.2 Lo wer Effectiveness 7.9 94.0 16.7 6.8 23.9 17.1 Higher Effectiveness 7.5 88.0 15.3 10.1 22.1 12.0 Lo wer Direct Costs 7.7 90.5 15.8 5.2 22.8 17.6 Higher Direct Costs 7.8 91.5 8.5 3.8 13.8 10.0 Wider Diesel Downsizing 8.9 104.7 9.9 4.0 15.9 11.9 07 Pct Discount Rate 9.0 96.3 15.3 7.2 22.7 15.5 07 Pct DR, 0 Month Payback 10.3 112.2 18.2 8.5 26.9 18.4 Allow Gas To Diesel 7.9 92.3 16.0 5.9 23.1 17.2 Allow Gas To Diesel, 0 Month Payback 7.3 85.8 15.1 6.9 21.7 14.8 Flat mix after 2016 8.4 99.8 17.6 7.4 25.4 17.9***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 52 

### 10.2.2.5 **Probabilistic Uncertainty Analysis** OMB Circular A - 4 directs agencies to conduct formal probabilistic uncertainty analy sis of complex rules where there are large, multiple uncertainties whose anal ys is raises technical challenges or where effects cascade and where the impacts of the rule exce ed $1 billion. The proposed increase in MD - HD vehicle fuel economy /GHG standards meets all of these criteria. As for previous rules, NHTSA has conducted an uncertainty analysis to determine the extent to which uncertainty

about input assumptions could imp act the costs and benefits attributable to the proposed rule. Throughout the course of the main anal ys is, input values were selected from a variety of often conflicting sources. Best estimates were selected based on the preponderance of data and anal ys es available, but there is inevitably a level of uncertainty in these selections, particularl y given the time frame of the rulemaking. Some of these inputs contributed less to the overall variations of the outcomes, and, thus, are less significant. Some input s depend on others or are closel y related (e.g., oil import externalities), and thus can be combined. With the vast number of uncertainties embedded in this regulatory anal ys is, this uncertainty analy sis identifies only the major independent uncertainty f actors having appreciable variability and impact on the end results and quantifies them by probability

distributions. The values of these uncertainties are then randomly

selected and fed back into the CAFE model to determine the net benefits using the Mont e Carlo statistical simulation technique. Using point estimates for the large number of variables in this analysis provides only a limited view of the potential results, and provides, likewise, a limited measure of confidence in the estimated outcome, beyond the assertion that it is the “most likely .” Correctl y estimating the exact total costs and benefits of a program as complex as the proposal, especiall y over such a long time frame, is, of course, not possible. This is why the direction in A - 4 sugg ests anal ys is of the sources and consequences of uncertainty in the results. Using Monte Carlo simulations to explicitly consider the uncertainty around the important inputs to the analysis, enables decision makers to see the probabilities associated with a large range of outcomes and develop confidence in achieving acceptable levels of net benefit from the existing program specification, even without perfect information about future conditions. Having confidence that a rule will perform as expected under a range of potential future states of the world is a valuable outcome. Unlike the preceding sensitivity analy sis, which is useful for understanding how alternative values of a single input assumption may influence the estimated impacts of the proposed standards, the uncertainty analysis considers multiple states of the world, characterized by specific values of all relevant inputs, based on their relative probability of occurrence. A sensitivity

anal ys is varies a single parameter of interest, holding all o thers constant at whatever nominal values are used to generate the single point estimate in the main analy sis, and measures the resulting deviation. However, the uncertainty analy sis allows all of those parameters to vary simultaneously

– relaxing the assumption that “all else is equal.” Each trial, of which there are 14,000 in this anal ys is, represents a different state of the world in which the standards are implemented. To gauge the robustness of the estimates of impacts in the proposal, NHTSA varied te chnology costs and effectiveness, fuel prices, market demand for fuel economy improvements in the absence of the rule, the amount of additional driving associated with fuel econom y improvements (the rebound effect), and the on - road gaps between realized fu el economy and laboratory

test values for gasoline and diesel vehicles. The***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 53 shapes and types of the probability distributions used in the anal ys is vary by uncertainty, though the costs and effectiveness values for technologies are sampled as groups to mini mize issues associated with interdependence. Similar technology

costs are sampled in such a way that they are simultaneously

at similar points in their respective cost distributions, even if the distributions themselves are different. For example, different levels of low rolling resistance tires might have different underlying distributions describing the degree of certainty associated with the point - estimate value of cost. The distributions of cost might be different to represent different degrees of technology

readiness (for example). For tires, however, one would expect advances in technology

to be shared across models, so the cost an advanced low rolling resistance tire would not be expected to diverge from the value in the main anal ys is. The sample de sign of technology costs (and effectiveness) for the proposal’s Monte Carlo anal ys is attempts to account for such similarities. The most important input to the uncertainty analysis, fuel prices (which drive the majority of benefits from the proposed stand ards), are drawn from a range of fuel prices characterized b y permutations of the L ow, Reference, and High fuel price cases in the Annual Energy Outlook 2014. 

### 10.2.2.5.1 Summary of Uncertainties Varied in Analysis NHTSA reviewed the inputs and relationships that driv e the CAFE model to identify the factors that are both uncertain and important to the estimation of net benefits. Several factors were identified as potentially contributing to uncertainty to the estimated impacts of higher CAFE standards, although not all were ultimatel y selected to be run in the simulation. In particular, the social cost of damages caused b y criteria pollutant and greenhouse gas emissions have been omitted from the anal ys is, the latter based on guidance from the interagency working group that developed the cost estimates used in the central anal ys is. The list of included uncertainties is: (1) Technology

costs; (2) Technology

effectiveness; (3) Fuel prices; (4) Manufacturers’ decision to produce vehicles with fuel economies higher than the levels mandated b y CAFE standards; (5) The rebound effect; (6) The on - road gap between achieved real - world fuel econom y and the test cycle for gasoline and diesel vehicles. 

### 10.2.2.5.2 Technology Costs and Effectiveness***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 54 The costs incurred b y manufacturers to modify th eir vehicles to meet new CAFE levels are assumed to be passed on to consumers in the form of higher new vehicle prices. These technology

costs are the primary

determinant of the overall cost of improving fuel economy. For each of the technologies considered applicable to this vehicle segment for the purpose of improving fuel economy , the agency used what it considered to be the most likely value in the main anal ys is. Unlike previous anal ys es of light - duty technology costs and effectiveness, that relied on a broader knowledge base, there are fewer studies of technology cost and effectiveness for medium - duty vehicles. As such, the distributions used to characterize the uncertainty are more agnostic than the set used in the last light - duty rule, for example. The cost distributions in this analysis are generall y very flat Beta distributions, which behave like uniform distributions with fuzzy

boundaries in practice

– appropriate since even the range of potential values is unknown. The effectiveness uncertainty , for the purpose of gauging compliance, is generall y characterized b y a normal distribution for each technology. The standard deviation of the normal distribution is based on the complexity assigned to the technology , where low, medium, and high complexity technologies are assigned normal distributions with standard deviations of 0.145, 0.29, and 0.435, respectively . Each draw results in a scalar that is used to modify the value in the main anal ys is through simple multiplication, values less than one represent lower cost/effectiveness, values greater than one represent higher cost/effectiveness. It is worth noting that cost and effectiveness are treated as independent

– so a technology may simultaneousl y be less expensive and more effective (or vice versa) th an anticipated in the main analy sis for a given simulation in this exercise. 

### 10.2.2.5.3 Fuel Prices For this anal ys is, fuel prices are sampled as a scaling factor that determines a complete time series of prices for all years covered b y the analy sis. The scaling fact or scales the inter annual differences of fuel prices in the High Oil Price case for the 2014 Annual Energy Outlook. Values within the sampled range produce series that have shapes similar to the high, reference, and low oil price cases, and are generally bounded above and below by the high and low price case, respectivel y. As E IA makes no claims about the relative likelihood of the fuel price cases, we make none here

– the range of values is sampled uniformly , suggesting that any single time series of pric es within that range is as likel y as another. 

### 10.2.2.5.4 Market-Driven Fuel Economy Improvements in the Absence of the

Proposed Standards The CAFE model includes the capability to appl y technology under varying fuel price cases b y including a variable that represents manufacturers’ assumption about consumer willingness - to - pay

for fuel econom y technology. In this case, “willingness - to - pay” is characterized as the pay

back period for fuel economy technology

investments, meaning the number of ye ars’ worth of fuel savings necessary to balance the cost of the new technology . In the main anal ys is, the model alters that variable to be zero once a manufacturer reaches compliance (for baseline 1a) or six months (for baseline 1b). In the case of baseline 1a, no additional technology

is added beyond the standards in the baseline, or in any of the other***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 55 regulatory

scenarios. In the case of baseline 1b, onl y those technologies whose fuel savings in the first six - months of ownership exceed the incremental cost of the technology would be added. For the main anal ys is, which uses a single fuel price projection and economic and technological parameter values cons istent with it, the zero - ye ar and six - month pay back assumptions are generall y consistent with the collection of other assumptions. However, under more extreme fuel price scenarios, short payback periods become increasingl y unrealistic. To address this lim itation, NHTSA has included the length of the pay back period (that manufacturers assume consumers desire) in the uncertainty analysis. Assuming some non - zero pay back period ensures that when fuel prices are very high, manufacturers will continue to add cos t - effective fuel economy technologies even when the standards are not sufficient to force these additions. As one might expect, higher fuel prices and longer pay back periods result in more fuel econom y technology being added beyond the level mandated b y th e standards, while lower fuel prices and shorter pay back periods result in less. The cases that most challenge internal consistency are naturall y found at the extremes, low - price - long - pay

back, for example. In the low - price - long - pay back case, the fuel savi ngs would still be very

low, technology

would still not be very

attractive, and only

a small amount of additional fuel econom y technology (if any at all) would be added to vehicles already in compliance. So one could credibl y argue that there is uncertainty about the degree to which manufacturers understand consumers’ willingness - to - pay for fuel economy and add slightl y more than would be demanded. Similarly , under the high - price - short - pay back draws, fuel prices are high enough to make some technology

addit ions occur in the baseline, but may be not the ideal amount under those conditions because manufacturers assumed a shorter pay back period than consumers have when faced with very

high fuel prices. Since there is uncertainty about manufacturers’ ability to p erfectly respond to consumer preferences, these draws produce results that are still plausible (if less probable than others). The pay back period used in the anal ys is is drawn from a Beta distribution with shape parameters equal to 2 and 5. This places ab out 75 percent of the probability distribution’s mass below 2 years, with steeply decreasing probabilities afterward. 

### 10.2.2.5.5 The Rebound Effect By reducing the amount of gasoline used and, thus, the cost of operating a vehicle, more stringent fuel econom y and GHG standards are anticipated to result in a slight increase in annual miles driven per vehicle. This rebound effect impacts net societal b enefits because the increase in miles driven offsets a portion of the fuel savings that results from more fuel - efficient vehicles. Although operators derive value from this extra driving, it also leads to increases in crashes, congestion, noise, and pollution costs associated with driving. On the basis of previous studies devoted to the impact of fuel economy changes on the vehicle miles traveled of comparable light - duty trucks, NHTSA emplo yed a rebound effect of 10 percent in the main anal ysis. A more complete discussion of the rebound effect is included in Chapter VIII. For the uncertainty analysis, a range of 5 to 30 percent was used and emplo yed in***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 56 a slightly skewed Beta distribution which produced a mean of approximately 14.2 percent. The differen ce reflects the more cost - conscious behavior of commercial vehicle operators, particularl y for HD pickups and vans which are often registered at residences and may be used to substitute VMT for other household vehicles. 

### 10.2.2.5.6 On -Road Gaps As is the case of fue l economy ratings for light - duty

vehicles, medium - duty work trucks can achieve different levels of fuel economy in real - world applications. Medium - duty vehicles may achieve lower fuel economy than their ratings because of driver habits (like faster acceleration) or terrain/conditions. However, unlike light - duty fuel economy, where vehicles are ty picall y used for moving people, even when driving styl e and conditions vary from the test procedure, medium - duty vehicles are typicall y used for multiple purposes. Vehicle usage

– in particular, loading

– is an additional source of bias. In order to estimate the impact of both driving profiles and vehicle loading on fuel consumption, we used simulation results from Southwest Research Institute , who conducted a simulation study

under a NHTSA contract to estimate the fuel consumption improvement associated with appl yi ng specific technologies to medium and heavy- duty trucks. 15 We then computed the difference between each driving cycle and the corresponding test cycle, then created a distribution from those differences for each fuel type (gasoline and diesel). The resulting distributions were multi - modal for both fuel ty pes, largel y as a result of the different sources of discrepancy

from the test cycle: city/highway spli ts, driving profile, vehicle loading, and a relatively

sparse set of results. To smooth the distributions, we split the computed gaps into two bins

– one of gaps (strictly) less than 0.2 and the other containing larger gaps. Then we bootstrapped the computed gaps by repeatedl y sampling (with replacement) from each bin, randomly choosing a weighting parameter from a uniform distribution spanning [0.3, 0.7], then taking a weighted average of the low and high gap bins to create a distribution. The resulting e mpirical distributions for both gasoline and diesel were then fit using gamma distributions, and the gamma distributions were sampled in the uncertainty anal ys is. 

### 10.2.2.5.7 Results Figure 10 - 6 display s the distribution of net benefits estimated by the ensemble of simulation runs. As Figure 10 - 6 indicates, th e analy sis produces a wide distribution of possible outcomes that are much broader than th e range of estimates characterized by onl y the difference between the more and less dy namic baselines. While the expected value, the probabilityweighted average outc ome, is only about 70 percent of the net benefits estimated in the main anal ys is, almost all of the trials produce positive net benefits. In fact, the distribution suggests there is only

a one percent chance of the proposal producing negative net benefits for HD pickups and vans. So , while the estimated net benefits in the main anal ys is may be higher than

15 Reinhart, T.E. (2015, June). Commercial Medium - and Heavy - Duty Truck Fuel Efficiency Technology Study

– Report #1. (Report No. DOT HS 812 146). Washington, DC: National Highway Traffic Safety Administration.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

10 - 57 the expected value when uncertainty is considered, net benefits at least as high as those estimated in the main anal ys is are still 20 times as likely as an outcome that results in net costs.

**Figure 10**  **- 6**  **Distribution of**  **Net Benefits from Proposed Standards for HD Pickups and Vans** Figure 10 - 7 shows the distribution of pay back periods (in years) for Model Year 2029 trucks across 14,000 simulation runs. The “pay back period” typically refers to the number of ye ars of vehicle use that occur before the savings on fuel expenditures offset the additional technology

cost associated with improved fuel economy . As Figure 10 - 7 illustrates , the incremental technology

cost of both Phase 1 and Phase 2 is eclipsed by the value of fuel savings by year three of ownership in most cases.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, or Release During Review *****

10 - 58 **Figure 10**  **- 7**  **Average Payback Period for MY 2029 HD Pickup or Van based on Phase 1 and Phase 2 (combined) Technology Costs**  This is an important metric for consumer acceptability and, though Figure 10 - 7 illustrates the long right tail of the pay back distribution (where pay back periods are likely

to be unacceptabl y long), fewer than ten percent of the trials result in payback periods longer than four ye ars. This sugg ests that, even in the face of uncertainty about future fuel prices and fuel econom y in real - world driving conditions, buyers of the vehicles that are modified to comply with the requirements of the proposal will still see fuel savings greater than their additional vehicle cost in a r elatively short period of time.