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Chapter 10: CAFE Model for HD Pickups and Vans

For this rule, the agencies conducted coordinated and complementary analyses using two 

analytical methods for the heavy-duty pick up and van segment by employing both DOT’s CAFE 

model and EPA’s MOVES model. For heavy-duty pickups and vans, the agencies performed 

complementary analyses, 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 analysis and 

Method B as a supplemental analysis. EPA considered the results of both methods. The 

agencies concluded that both 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 chapter, the CAFE modeling system is described and used to analyze technology

use and per-vehicle costs under each regulatory alternative, including the no action alternative 

(which reflects continuation of previously-promulgated standards). However, this model is more 

comprehensive and also projects other impacts. NHTSA addresses these other impacts in the 

Draft EIS and these are also presented here.1

10.1 HD Pickup and Van Fleet

10.1.1 Why did the Agencies Develop the Analysis Fleet?

The modeling system relies on many inputs, including an analysis 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 standards, accounting for 

the mix and fuel consumption of vehicles in each manufacturer’s regulated fleet. The agencies 

create an analysis fleet in order to track the volumes and types of fuel economy-improving and 

CO2-reducing technologies that are already present in the existing vehicle fleet. This aspect of 

the analysis 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 benefits. 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 develop the analysis 

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.
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10.1.2 How the MY2014 Based Analysis Fleet Was Developed?

Most of the information about the vehicles that make up the 2014 analysis 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 Heavy-Duty Trucks, 

MYs 2014-2018.

The major manufactures of class 2b and class 3 trucks (Chrysler, Ford and GM) were 

asked to voluntarily submit updates to their Pre-Model Year Reports. Updated data were 

provided by Chrysler and GM. These updated data were used in constructing the analysis fleet 

for these manufacturers.

The agencies agreed to treat this information as Confidential Business Information (CBI) 

until the publication of the NPRM. This information can be made public at this time because by 

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 Type, Axle Ratio, Work Factor, Curb Weight2, Test 

Weight3, GVWR, GCWR, Fuel Consumption (gal/100 mile)4, engine type (gasoline or diesel), 

engine displacement, transmission type and number of gears5.

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 changed 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 weight 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 rounded 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 

submission Fiat provided “Fuel Economy on Primary Fuel (Unadjusted combined CO2 g/mi).” These values were 

converted to mpg using the factors 8,887 gCO2/gal for gas engines and 10.180 gCO2/gal for diesel engines.

5 GM did not provide transmission data in its submittal. 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.”
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In addition to information about each vehicle, the agencies need additional information 

about the fuel economy-improving/CO2-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 sheet 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 Economy 

(Ethanol-85)” and “Fuel Economy (Biodiesel-20)” of the CAFE market data input file. 

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 Class, 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 by the CAFE model. These 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 their 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. Horsepower, 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, 

Compression 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 GVWR 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.
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The Structure values (Ladder, Unibody) used by 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 the term “Uniladder” to describe the structure. Vans based on this structure are 

noted in the Vehicle Notes column of the NHTSA input file.

Style values used by the CAFE model were also added: Chassis Cab, Cutaway, Pickup 

and Van.

10.1.2.1Vehicle Redesign Schedules & Platforms

10.1.2.2Pickup Trucks

Product cadence in the Class 2b and 3 pickup market has historically ranged from 7-9 

years between major redesigns. However, due to increasing competitive pressures and consumer 

demands the agency anticipates that manufacturers will generally 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 2027. 

10.1.2.2.1 Ford 

In the 2b/3 pickup truck market, Ford produces the F250, F350 and F450, currently 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 

General 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-year product cadence in the HD truck market, with redesigns in 2015 and 2021.

10.1.2.2.3 Fiat (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.
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10.1.2.3 Vans

The 2b/3 van market has changed markedly 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 analysis represents a period where most manufacturers, with the 

exception of General Motors, have recently 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 analysis is approximately ten 

years between major redesigns, allowing manufacturers’ only 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 after 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 Fiat (Ram)

The product cycle of the van from the Ram brand has less of a historical precedent. Fiat 

currently offers the Promaster (a variant of the Ducato van sold in other markets). Previously 

Chrysler sold Sprinter vans in an agreement with Daimler from 2003, and had previously 

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 publications and internet sources suggest the nextgeneration Nissan Titan could debut in model year 2016, and the agencies anticipate that the NV 
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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 year 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 salesweighted averages of the fuel economy/GHG 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 analysis, 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 from the 2014 Annual Energy

Outlook Reference Case and IHS Automotive to determine model variant level sales volumes in 

future years.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 analysis, the agencies have limited this analysis fleet to class 2b and 3 HD 

pickups and vans. However, especially considering interactions between the light-duty and HD 

pickup and van fleets (e.g., MDPVs being included in the light-duty fleet), the agencies could 

also evaluate the potential to analyze the fleets in an integrated fashion.

 

9 Tables from AEO’s forecast are available at http://www.eia.gov/oiaf/aeo/tablebrowser/. The agencies also made 

use of the IHS Automotive Light Vehicle Production Forecast (August 2014).
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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 year volumes have been used to represent the corresponding model-year 

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-year compliance 

data.

The relative sales share by vehicle type (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 projection. Table 10-1 shows the implied shares of the total new 2b/3 

vehicle market broken down by manufacturer and vehicle type. 
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Table 10-1 IHS Automotive Market Share Forecast for 2b/3 Vehicles

MODEL YEAR MARKET SHARE

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 applying the model-variant’s share of manufacturer sales in the premodel-year compliance data for the relevant vehicle style, and multiplied by the total volume 

estimated for that manufacturer and that style.

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 by either the existing sales forecasts or the simulated technology changes 

in vehicle platforms. In particular, Ford has announced intentions to phase out their existing 

Econoline vans, gradually shifting volumes to the new Transit platform for some model variants 

(notably chassis cabs and cutaways variants) and eliminating offerings outright for complete 

Econoline vans as early 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 L diesel, respectively. Within each powertrain, sales were allocated 

based on the percentage shares present in the pre-model-year compliance data. The chassis cab 

and cutaway 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.

10.1.2.5Selected Characteristics of the MY2014 Based Analysis Fleet

The tables below summarize some of the characteristics of the MY2014 based analysis 

fleet for Class 2b and Class 3 trucks.

Table 10-2 shows production by manufacturer and indicates that Ford is dominant with 

52 percent of this market.
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Table 10-2 Estimated 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 shows 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 Estimated 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 shows production by style or body type. Pickup trucks make up 52 percent of 

production and vans 42 percent of production.

Table 10-4 Estimated MY2014 Production by Vehicle Style

STYLE PRODUCTION PERCENT

Chassis Cab 19,724 3.1%

Cutaway 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.
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Table 10-5 Estimated 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 shows production by drive type with an almost equal division between two 

wheel drive (55 percent) and four wheel drive (45 percent).

Table 10-6 Estimated 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 analysis 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 Estimated 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%
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Table 10-8 Estimated 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 style in Class 2b and 3 trucks. 

Table 10-9 shows pickup truck production by manufacturer. Only 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 Estimated 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 shows 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, Daimler and Nissan.

Table 10-10 Estimated 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 10-11shows the distribution of diesel trucks by manufacturer. Ford is the 

leader at 40 percent followed by Fiat at 34 percent. 
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Table 10-12 shows diesel production as a percent of total 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 by General Motors at 32 percent and Ford at 30 percent.

Table 10-11 Estimated 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 Estimated 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 analysis fleet is provided in detail at NHTSA’s web site, along with all 

other inputs to and outputs from today’s analysis. 

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 year 2018. The agencies estimated 

the extent to which manufacturers might add fuel-saving (and, therefore, CO2-reducing) 

technologies under each regulatory alternative, including the no-action alternative defined by 

Phase 1 standards. NHTSA also used the CAFE model to estimate the extent to which this 

additional technology would incrementally (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 

analysis, and explains the selection of the preferred alternative defined by today’s proposed 

standards.
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As discussed above, the agencies are proposing standards defined by fuel consumption 

and GHG targets that continue through model year 2020 unchanged from model year 2018, and 

then increase in stringency at an annual rate of 2.5 percent through model year 2027. In addition 

to this regulatory alternative, the agencies also considered a no-action alternative under which 

standards remain unchanged after model year 2018, as well as three other alternatives, defined by 

annual stringency increases of (1) 2.0 percent, (2), 3.5 percent, and (3) 4.0 percent during model 

years 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 by multiplying the model year 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 INCREASE

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 analysis of potential standards for HD pickups and vans, the agencies have 

applied DOT’s Corporate Average Fuel Economy (CAFE) Compliance and Effects Modeling 

System (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 analyses 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 

significantly 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 analyses 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 Office (GAO) and National Research Council (NRC). NHTSA makes public the 

model, source code, and—except insofar as doing so would compromise confidential business 
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information (CBI) manufacturers have provided to NHTSA—all model inputs and outputs 

underlying published rulemaking analyses.10

Although the CAFE model can also be used for more aggregated analysis (e.g., involving 

“representative vehicles”, single-year snapshots, etc.), NHTSA designed the model with a view 

toward (a) detailed simulation of manufacturers’ potential actions given a defined set of 

standards, followed by (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 assumptions and estimates, not to predict actions manufacturers will take.

As a starting point, the model makes use of an input file defining the analysis fleet—that 

is, a set of specific vehicle models (e.g., Toyota Tacoma) and model configurations (e.g., Toyota 

Tacoma with 4.0-liter V6 engine, 4WD, and 5-speed manual transmission) estimated or assumed 

to be produced by each manufacturer in each model year to be included in the analysis. The 

analysis fleet includes key engineering attributes (e.g., curb weight, 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 specific 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 payback period used to estimate the potential that each manufacturer might apply 

technology to improve fuel economy beyond levels required by standards.

A second input file to the model contains a variety of contextual estimates and 

assumptions. Some of these 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, 

vehicular and upstream emission factors, the social cost of carbon dioxide emissions, and the 

discount rate, are relevant to calculating physical 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 

efficacy 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 “synergy factors” used to make adjustments accounting for the potential 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 year-by-year basis separately for each regulatory class (passenger cars, light trucks, 

and heavy-duty pickups and vans). Regulatory alternatives are specified as discrete scenarios, 

 

10 Analyses can be found at http://www.nhtsa.gov/fuel-economy
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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 year-by-year 

application of fuel-saving technologies to each engine, transmission, and vehicle. Subject 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 practicably 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 outlays), 

continuing to add improvements as long 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 physical 

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 physical 

impacts.

This analysis reflects 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 changes specifically enable analysis of potential fuel consumption standards (and, hence, 

related CO2 emissions standards harmonized with fuel consumption standards) for heavy-duty 

pickups and vans; other changes implement more general improvements to the model. Key

changes include the following:

 Expansion and restructuring of model inputs, compliance calculations, and reporting to 

accommodate standards for heavy-duty pickups and vans, including attribute-based 

standards involving targets that vary with “work 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 

heavy-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 correspondingly 

update calculated work factors.

 Expansion of model inputs, procedures, and outputs to accommodate technologies not 

included in prior analyses.

 Changes to the algorithm used to apply technologies, enabling more explicit accounting 

for shared vehicle platforms and adoption and “inheritance” of major engine changes.
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 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 

technical, 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, early versions of the model added explicit “carrying forward” of applied technologies 

between model years, 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 technology 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 increases 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 example, even if a manufacturer is expected to redesign half of its 

production in MY 2016, if the manufacturer is not already producing any strong hybrid electric 

vehicles (SHEVs), a phase-in cap can be specified in order to assume that manufacturer will stop 

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 analysis accompanying the 2012 final rule regarding 

post-2016 CAFE standards and related GHG emissions standards, DOT 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 & Technology

The term “platform” is used loosely in industry, but generally refers to a common 

structure shared by a group of vehicle variants. The degree of commonality varies, with some 

platform variants exhibiting traditional “badge engineering” where two products are 

differentiated by little more than insignias, while other platforms be used to produce a broad 

suite of vehicles that bear little outer resemblance to one another.
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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 nearly “bolt-on” technologies, others involve substantial 

changes to the structure and design of the vehicle, and therefore necessarily 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 aerodynamic 

technologies. As the vehicle applies technologies, it effectively 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 years. 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 deployment at the highend, 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 by manufacturers: typically 1-2 truck platforms and 1-2 van platforms. 

However, accounting for platforms will take on greater significance in future analyses involving 

the light-duty fleet, 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. Typically 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.
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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 technologies 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 analysis spanning 

different regulatory classes, accounting both for standards that apply separately to different 

classes and for interactions between regulatory classes. Light 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 passenger cars and 

4WD versions classified as light trucks. Integrated analysis 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 analysis 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 mediumduty passenger vehicles (MDPVs) and thus included in manufacturers’ light-duty truck 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 

analysis supporting the planned final rule, the agencies intend to develop an overall analysis 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 generally, modeling the two fleets 

together should tend to more realistically limit the scope and complexity of estimated 

compliance pathways.

NHTSA anticipates that the impact of modeling a combined fleet will primarily arise 

from engine-transmission inheritance. 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 only one engine-transmission leader can be defined across the combined fleet. As with 
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the fleets separately, all vehicles using a shared engine/transmission would automatically adopt 

technologies adopted by 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), thereby 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, especially in years 

where many models may be scheduled for refresh or redesign. The newly-introduced 

representation platform-, engine-, and transmission-related considerations discussed above 

augment the model’s preexisting representation of redesign cycles and accommodation of phasein caps. Considering these new constraints, inputs for today’s analysis de-emphasize reliance on 

phase-in caps.

In this application of the CAFE model, phase-in caps are used only for the most advanced 

technologies included in the analysis, i.e., SHEVs and lean-burn GDI engines, considering that 

these 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 of 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 subsequently increasing at 5 percent annually for four years 

(reaching 50 percent in MY 2030). Note that the 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 Technology Application Requirements

Compared to prior analyses 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 to manufacturers’ fleets. First, since the number of HD 

pickup and van platforms in a portfolio is typically small, compliance with standards may appear 

especially “lumpy” (compared to previous applications of the CAFE model to the more highly 

segmented light-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 deployment remains tied strongly to estimated product redesign and freshening 
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schedules, technology penetration rates may jump more quickly as manufacturers apply 

technology to high-volume products in their portfolio.

By design, restrictions that enforce commonality of mass reduction and aerodynamic 

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 accurately 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 analysis fleet used for today’s 

analysis. Results for GM clearly reflect the analysis 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 analysis suggests that GM could take some 

advantage of credit carry-forward provisions (e.g., to cover a shortfall projected to occur in MY 

2016, when the HD pickup and van fleet first includes some vehicles not previously subject to 

chassis dynamometer testing), but that GM could need to significantly over comply with 

standards during MYs 2021-2024 in order take full advantage of the 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 analysis). 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.
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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 

deactivation, 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
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 6.0 liter gasoline engine: apply lower-friction lubricants and engine friction reduction

 All Sierra/Silverado pickups: apply 5 percent mass reduction, aerodynamic

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: apply integrated starter-generators

 24 percent of Sierra/Silverado pickups: apply strong hybrid-electric systems (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: apply strong hybrid-electric systems (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.
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As discussed above, these results provide an estimate, based on analysis 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 analytical inputs). Still, 

the example should serve to illustrate how various 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 model 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 economy 

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 

accompanying 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),

∆FCunrounded_TW = % change in fuel consumption (from model input), without TW 

rounding,

∆TW = % change in test weight (calculated), and

∆FCrounded_TW = % change in fuel consumption (calculated), with TW rounding.

 

11 Or, equivalently, CW + 1⁄2 payload, where payload = GVWR – CW.
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As a result, some applications of vehicle mass reduction will produce no compliance 

benefit at all, 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 not taken 

into account, in cases where even small changes in ALVW are sufficient to cause vehicles’ test 

weights to increase by, 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 year, 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 mass.

In addition, considering that the regulatory alternatives in the agencies’ analysis all 

involve attribute-based standards in which underlying fuel consumption targets vary with “work 

factor” (defined by 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 apply 

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 reduced, manufacturers are likely to “return” some of the change to payload 

and/or towing capacity. For this analysis, the agencies have applied the following assumptions:

GVWR will be reduced by half the amount by 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 ASSUMED 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 hybrid 
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electric vehicles as reducing payload by 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 comment on these methods for estimating how changes in vehicle 

mass may impact fuel consumption, GVWR, and GCWR, and on corresponding inputs to today’s 

analysis.

10.2.2 What Impacts Did the Agencies’ Analysis Show for Different 

Regulatory Alternatives

10.2.2.1 Industry Impacts

As discussed above, the agencies’ analysis fleet provides a starting point for estimating 

the extent to which manufacturers might add fuel-saving (and, therefore, CO2-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 analysis 

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 analysis fleet includes some significant changes relative to fleet information 

underlying analysis supporting the establishment of Phase 1 standards applicable starting in 

model year 2014; in particular, the analysis fleet includes some new HD vans (e.g., Ford’s 

Transit and Fiat/Chrysler’s Promaster) that are considerable more fuel-efficient than HD vans 

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 likely to influence planning decisions made by manufacturers 

several years before they begin, as illustrated by example above. This is true in light-duty 

planning, but accentuated by the comparatively 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 vehicles (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 by 

the proposed standards lead to additional technology application to vehicles in the analysis fleet 

that occurs in the years 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 baseline 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.
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Table 10-13 MY2021 Baseline Costs for Manufacturers in 2b/3 Market Segment 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 by the 

Phase 1 standards. Reflecting differences in projected product offerings in the analysis 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 

Chrysler/Fiat) do not include the costs of their recent van redesigns, which are already present in 

the analysis 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 payback period in lieu of the 6-

month payback period discussed above. (A sensitivity analysis using Method A, discussed 

below, also explores longer payback periods, as well as the combined effect of payback period 

and fuel price on vehicle design decisions.) Resultant 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 payback period:

Table 10-14 MY2021 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 by 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 regulatory action. Large-scale, 
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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 by the 

Phase 1 MDHD rulemaking that covers model years 2014 – 2018 and is constant from model 

year 2018 through 2020. In the baseline scenario, the Phase 1 standards are assumed to remain 

in place and at 2018 levels throughout the analysis (i.e. MY 2030). The only difference between 

the definitions 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 alternatives (1 – 5), but as discussed 

above, all of the standards are based on the curve developed in the Phase 1 standards that relate 

fuel economy 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 1b) for Model Year 203012, with the 

exception of technology penetration rates, which are absolute. 

The technologies 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 likely to be offered in MY2030 2b/3 vehicles. The summaries of technology

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-hybridization (which reduces engine idle, but does not assist 

propulsion), full hybridization (integrated starter generator or strong hybrid that assists 

propulsion and recaptures braking energy), and aerodynamic 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 example.

 

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. 
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Table 10-15 Summary of HD Pickup and Van Alternatives’ Impact on Industry 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

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

Achieved 19.14 20.61 20.83 21.27

Average Fuel Consumption (gallons /100 mi.)

Required 5.25 4.86 4.86 4.73

Achieved 5.22 4.85 4.80 4.70

Average Greenhouse Gas Emissions (g/mi)

Required 495 458 458 446

Achieved 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

Hybridizationa0 8 35 51

Aero. Improvements 36 78 78 78

Mass Reduction (vs. No-Action)

CW (lb.) 239 243 325 313

CW (%) 3.7 3.7 5.0 4.8

Technology Cost (vs. No-Action)

Average ($) b578 1,348 1,655 2,080

Total ($m) c437 1,019 1,251 1,572

Payback period (m) c25 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 relatively small increases in required 

fuel economy and average incremental technology cost between the alternatives, suggesting that 

the challenge of improving fuel consumption and CO2 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 by 

about 3 percent between Alternative 4 and Alternative 5, average technology cost increases by
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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 an identical

required fuel economy improvement leading to price increases greater than 20 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 slower 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 fairly constant at around an additional three months for each 

jump in stringency.

Manufacturers offer few models, typically only 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 impractical or 

impossible. This forces manufacturers to apply some 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 some 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, narrowly applying 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 hybridization resulting from the implementation of the standards. While only

about 8 percent full hybridization (defined as either integrated starter-generator or strong hybrid) 

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 hybridization 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 subject to Alternative 3 achieves the same fuel economy as new 

vehicles subject to Alternative 4, with less hybridization 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 Chrysler (Fiat), 

are expected to have approximately 95 percent of the 2b/3 new vehicle market during the years 

that the proposed standards are in effect. Due to their importance to this 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 Chrysler/Fiat, respectively.
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Table 10-16 Summary of Impacts on General Motors 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 18.38 19.96 20 20.53

Achieved 18.43 19.95 20.24 20.51

Average Fuel Consumption (gallons /100 mi.)

Required 5.44 5.01 5 4.87

Achieved 5.42 5.01 4.94 4.87

Average Greenhouse Gas Emissions (g/mi)

Required 507 467 467 455

Achieved 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

Hybridizationc0 19 79 100

Aero. Improvements 100 100 100 100

Mass Reduction (vs. No-Action)

CW (lb.) 325 161 158 164

CW (%) 5.3 2.6 2.6 2.7

Technology Cost (vs. No-Action)

Average ($) a785 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.
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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

Achieved 19.5 21.04 21.28 21.8

Average Fuel Consumption (gallons /100 mi.)

Required 5.15 4.77 4.78 4.65

Achieved 5.13 4.75 4.70 4.59

Average Greenhouse Gas Emissions (g/mi)

Required 485 449 450 438

Achieved 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

Hybridizationc0 2 14 30

Aero. Improvements 0 59 59 59

Mass Reduction (vs. No-Action)

CW (lb.) 210 202 379 356

CW (%) 3.2 3 5.7 5.3

Technology Cost (vs. No-Action)

Average ($) a506 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.
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Table 10-18 Summary of Impacts on Fiat/Chrysler 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 18.73 20.08 20.12 20.70

Achieved 18.83 20.06 20.10 20.70

Average Fuel Consumption (gallons /100 mi.)

Required 5.34 4.98 4.97 4.83

Achieved 5.31 4.99 4.97 4.83

Average Greenhouse Gas Emissions (g/mi)

Required 515 480 479 466

Achieved 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

Hybridizationc0 3 3 10

Aero. Improvements 0 100 100 100

Mass Reduction (vs. No-Action)

CW (lb.) 196 649 648 617

CW (%) 2.8 9.1 9.1 8.7

Technology Cost (vs. No-Action)

Average ($) a434 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 by 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 
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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 economy levels among manufacturers. In particular, 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 showing considerably 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 increases 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 

75 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/Chrysler. And 

for the most stringent alternative considered, General Motors would face average cost increases 

of more than $2,700, in addition to the more than $700 increase in the baseline – approaching 

nearly $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 Chrysler/Fiat, 

but reduces average vehicle mass by over 300 pounds in the baseline – suggesting that some of 

GM’s easiest Phase 1 compliance opportunities can be found in lightweighting technologies. 

Similarly, Chrysler/Fiat applies less hybridization than the others, and much less than General 

Motors, which is simulated to have hybrids (either integrated starter generator or full hybrid 

system) on much of its fleet by 2030, nearly 20 percent of which will be strong hybrids, 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 analysis 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 understand the differences in incremental technology costs and fuel economy 

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 factor, 

made more obvious in the following figures, is the difference between the fuel economy and 

performance of the recently redesigned vans offered by Fiat/Chrysler and Ford (the Promaster 

and Transit, respectively), and the more traditionally-styled vans that continue to be offered by 

General Motors (the Express/Savannah). In MY 2014, Ford began the phase-out of the 

Econoline van platform, moving those volumes to the Euro-style 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 

economy 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 analysis fleet, meaning they are not carried as part of the compliance 

modeling exercise. By contrast, General Motors is simulated to redesign their van offerings after 
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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 Ford and Chrysler/Fiat products in the analysis fleet is the primary driver 

of the cost discrepancy between GM and its competitors in both the baseline and Alternative 2, 

when Ford and Chrysler/Fiat have to apply considerably less technology to achieve compliance. 

Figure 10-3 and Figure 10-4 show the relationship between work factor and fuel 

economy for the model variants offered by GM, Ford and Chrysler/Fiat for gasoline (Figure 

10-3Error! 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 by 

crosses and pickup trucks by circles, with a different color corresponding to each member of the 

Big 3 (blue, green, and orange for Fiat/Chrysler, Ford, and General Motors, respectively). 

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 gasolinepowered Promaster, both have real separation from GM’s van offerings which are generally

higher in work factor and considerably lower in fuel economy. 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 

economy 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 economy 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. 
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Figure 10-3 Comparison 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

Fuel Economy on Gasoline

Manufacturer

Fiat

Ford

General Motors

Bosdy Style

Pickup

Van
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Figure 10-4 Comparison 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 considering 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 less 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

Fuel Economy on Diesel

Manufacturer

Fiat

Ford

General Motors

Bosdy Style

Pickup

Van
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Figure 10-5 Comparing sales mix for Chrysler/Fiat, GM, and Ford

Since larger work factors correspond to lower fuel economy 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 by the tendency of fuel consumption to increase as vehicle payload and towing capacity 

increase, and when 4WD is added. Figure 10-5 shows Chrysler/Fiat with the most favorable 

position, from the perspective of work factor, followed by GM. Although Ford’s sales mix of 

work factors is the least favorable, Ford’s sales mix generally has the highest fuel economy 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 economy vehicles, and generally outperforms both other manufacturers at each fuel 

economy 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 have consistently lower fuel economy than Ford and Chrysler 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 Chrysler/Fiat have about 40 percent and 10 percent of sales, respectively, at higher 

levels of fuel economy. Some of this discrepancy could be explained by measurement error in 

the pre-model-year compliance data; GM’s final submission for model year 2014 may contain 

higher fuel economies as a result of more direct 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 analysis 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. 
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The vans offered by both manufacturers currently 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 only really improves some of their transmissions of its 

Sprinter vans. By contrast, Nissan’s starting position is much weaker and their compliance costs 

closer to the industry average in Table 10-15. This difference could increase if the analysis fleet 

supporting the final rule includes forthcoming Nissan HD pickups.
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Table 10-19 Summary of Impacts on Daimler 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 23.36 25.19 25.25 25.91

Achieved 25.23 25.79 25.79 26.53

Average Fuel Consumption (gallons /100 mi.)

Required 4.28 3.97 3.96 3.86

Achieved 3.96 3.88 3.88 3.77

Average Greenhouse Gas Emissions (g/mi)

Required 436 404 404 393

Achieved 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

Hybridizationc0 0 0 0

Aero. Improvements 0 0 0 0

Mass Reduction (vs. No-Action)

CW (lb.) 0 0 0 0

CW (%) 0 0 0 0

Technology Cost (vs. No-Action)

Average ($) a0 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.
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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

Achieved 19.84 21.17 21.19 21.51

Average Fuel Consumption (gallons /100 mi.)

Required 5.09 44.72 4.78 4.66

Achieved 5.04 4.72 4.72 4.65

Average Greenhouse Gas Emissions (g/mi)

Required 452 419 425 414

Achieved 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

Hybridizationc0 0 0 28

Aero. Improvements 0 100 100 100

Mass Reduction (vs. No-Action)

CW (lb.) 0 0 307 303

CW (%) 0 0 5 4.9

Technology Cost (vs. No-Action)

Average ($) a378 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.
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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 – containing 

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 analysis.

Although Daimler’s required fuel economy 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. mass 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 consumer impacts of the rule are more straightforward. Table 10-21 shows the 

impact on the average consumer who buys a new class 2b or 3 vehicle in model year 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.
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Table 10-21 Summary of Individual Consumer Impacts in MY 2030 in the HD Pickup and Van2b3 Market 

Segment 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 monotonically 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 by 

consumers at the cost of the additional fuel plus the consumer surplus (a measure of the increase 

in welfare that consumers achieve by having more mobility). The refueling benefit measures the 

value of time saved through reduced refueling events, the result of improved fuel economy and 

range in vehicles that have been modified in response to the standards. 

There are some limitations to using payback period as a measure, as it accounts for fuel 

expenditures and incremental costs associated with taxes, registration fees and financing, and 

increased maintenance costs, but not the cost of potential repairs or replacements, which may or 

may not be more expensive with more advanced technology. 
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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, respectively. 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

Social benefits increase with the increasing stringency of the alternatives. As in the 

consumer analysis, the net benefits continue to increase with increasing stringency – suggesting 

that benefits are still increasing faster than costs for even the most stringent 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

ALTERNATIVE 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

CO2 emissionsb1.9 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 values are discounted at a rate of 3 percent per year from the time of 

purchase. 

Notes:
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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 CO2 value. There are four distinct social cost of 

CO2 values presented in the Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis under Executive Order 12866 (2010 and 2013). The CO2

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 –

MY2029 (although the early years of the series have nearly 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 society 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 carbon is used in this analysis. Just as the previous two externalities are 

caused by fuel consumption, others are caused by 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 costs 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).

Another side effect of increased VMT is the likely increase in traffic fatalities, which is a 

function of the total vehicle travel in each year. As 

Table 10-22 illustrates, the positive social cost associated with traffic fatalities is the 

result of an additional -10 (implying that Alternative 2 actually leads to a reduction in fatalities 

over the baseline, due to the application of mass reduction technologies), 35, 36, and 66 fatalities 

for Alternatives 2-5, respectively. To put those numbers in context, the baseline contains nearly

25,000 fatalities attributable to 2b/3 vehicles over the same period. The incremental fatalities 

associated with the alternatives translate to less than -0.4, 0.1, 0.1, and 0.3 percent increases over 

the MY2015-2029 baseline, respectively.

The CAFE model was used to estimate the emissions impacts of the various alternatives 

that are the result of lower fuel consumption, but increased 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 CO2 and criteria pollutants, see NHTSA’s accompanying Draft Environmental 

Impact Statement.
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produced in model years subject to the standards in the alternatives. Criteria pollutants are 

largely the result of vehicle use, and accrue on a per-mile-of-travel basis, but the alternatives still 

generally 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 incremental 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 

Segment, 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

CO2 (MMT) 54 91 110 127

CH4 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 NOX 23,800 43,600 53,500 62,200

PM 1,470 2,550 3,090 3,590

SO2 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 NOX 1.1 2.1 2.6 3.0

PM 1.7 3.0 3.6 4.2

SO2 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 dynamic 

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 analysis were compared 

against a flat baseline. This other comparison is summarized below, but both comparisons are 

discussed in greater detail in the Draft EIS.
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Table 10-24 Summary of Environmental Impacts Through MY2029 in the HD Pickup and Van Market 

Segment, 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

CO2 (MMT) 66 105 127 142

CH4 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 NOX 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 NOX 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 analysis to reveal whether, and to 

what extent, the results of the analysis are sensitive to plausible changes in the main assumptions 

and numeric inputs.”14 Considering this guidance, a number of sensitivity analyses were 

performed using analysis Method A to examine important assumptions and inputs, including the 

following:

1. Payback Period: In addition to the 0 and 6 month payback periods discussed above, also 

evaluated cases involving payback periods of 12, 18, and 24 months.

2. Fuel Prices: Evaluated cases involving 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 Payback Period: Evaluated one side case involving a 0 month payback 

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/.
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4. Benefits to Vehicle Buyers: The main Method A analysis assumes there is no loss in 

value to owner/operators resulting from vehicles that have an increase in price and higher 

fuel economy. NHTSA performed this sensitivity analysis 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 the calculated benefits they will achieve at 75, or 50 

percent, respectively, of the main analysis estimates. (These are labeled as 

75pctOwner/operatorBenefit and 50pctOwner/operatorBenefit.)

5. Value of Avoided GHG Emissions: Evaluated side cases involving lower and higher 

valuation of avoided CO2 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, 15PctReboundEffect 

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 by 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 by 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 5th and 95th percent points of the confidence interval estimated in the 

underlying statistical analysis. (These are labeled MassFatalityCoeff05pct and 

MassFatalityCoeff95pct.)

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 payback periods.

11. Diesel Downsizing: Evaluated a side case in which downsizing of diesel engines was 

estimated 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 inputs 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 summarizes key metrics for each of the cases included in the

sensitivity analysis using Method A for the proposed alternative. The table reflects the percent

change in the metrics (columns), relative to the main analysis, 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 analysis, divided by the difference between the 

preferred alternative and the baseline in the main analysis. Or, 
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  =

∆   − ∆  

∆  

∙ 100

Each metric represents the sum of the impact of the preferred alternative over the model 

years 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 Segment using Method A and versus the Dynamic Baseline, Alternative 1b (Cells are 

percent change from base case)

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-Low 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-Low, 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%

Low SCC 0.0% 0.0% 0.0% 0.0% -10.6% -14.1%

Low 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%
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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 Payback

8.2% 9.8% 11.5% -1.2% 11.3% 15.4%

Lower 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%

Lower 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 easily 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 physical 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 payback period involve 

different degrees of fuel consumption improvement, and these differences are greatest in the noaction alternative defining the baseline. Because all estimated impacts of the proposed standards 

are shown as incremental values relative to this baseline, longer payback periods correspond to 

smaller estimates of incremental impacts, as fuel economy increasingly improves in the absence 

of the rule and manufacturers are compelled to add less technology in order to comply with the 

standards.

Second, cases involving different fuel prices similarly involve different degrees of fuel 

economy improvement in the absence of the standard, as more, or less, improvement occurs as a 

result 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, 
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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 payback period and fuel price inputs work in opposing directions, the 

relative magnitude of each is important to consider for the combined sensitivity cases. While the 

low price and 0-month payback case leads to significant volumetric savings compared to the 

main analysis, the low fuel price is still sufficient to produce a negative change in net benefits. 

Similarly, the high price and 24-month payback 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 involving 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 analysis that excludes strong hybrids and includes 

the use of downsized turbocharged engines in vans currently equipped with large V-8 engines. 

The modeling results for this analysis are provided in Section IV.C.8 and in the table above. The 

no strong hybrid analysis shows that GM could comply with the proposed preferred Alternative 3 

without strong hybrids based on the use of turbo downsizing on all of their HD gasoline 

vans. Alternatively, when the analysis is modified to allow for wider application of diesel 

engines, strong HEV application for GM drops slightly (from 19 percent to 17 percent) in 

MY2030, average per-vehicle costs drop slightly (by about $50), but MY2030 additional 

penetration rates of diesel engines increase by about 10 percent. Manufacturer-specific model 

results accompanying 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 cases, 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 hybrids.

Fifth, the cases that vary the effectiveness and direct cost of available technologies 

produce nuanced results in the context of even the 0-month payback case. In the case of 

effectiveness changes, both sensitivity cases result in reductions to the volumetric fuel savings 

attributable to the proposal; lower effectiveness because the technologies applied in response to 

the standards save less fuel, and higher effectiveness because more of the increase in fuel 

economy occurs in the baseline. However, for both cases, social costs (a strong proxy 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. Higher 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 analysis 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.
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Table 10-26: Costs and Benefits of Proposed Standards for HD Pickups and Vans Under Alternative 

Assumptions

Sensitivity Case

Fuel 

Savings 

(billion 

gallons)

CO2 

Reduction 

(MMT)

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-Low 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-Low, 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

Low SCC 7.8 91.5 15.9 5.8 20.5 14.8

Low 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

Lower 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

Lower 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
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10.2.2.5 Probabilistic Uncertainty Analysis

OMB Circular A-4 directs agencies to conduct formal probabilistic uncertainty analysis 

of complex rules where there are large, multiple uncertainties whose analysis raises technical 

challenges or where effects cascade and where the impacts of the rule exceed $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 impact the costs and benefits attributable to the 

proposed rule. Throughout the course of the main analysis, input values were selected from a 

variety of often conflicting sources. Best estimates were selected based on the preponderance of 

data and analyses available, but there is inevitably a level of uncertainty in these selections, 

particularly 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 inputs depend on others 

or are closely related (e.g., oil import externalities), and thus can be combined. With the vast 

number of uncertainties embedded in this regulatory analysis, this uncertainty analysis identifies 

only the major independent uncertainty factors 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 

Monte 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.” Correctly estimating the 

exact total costs and benefits of a program as complex as the proposal, especially over such a 

long time frame, is, of course, not possible. This is why the direction in A-4 suggests analysis 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 decisionmakers 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 analysis, 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 analysis varies a single parameter of interest, holding all others constant at whatever 

nominal values are used to generate the single point estimate in the main analysis, and measures 

the resulting deviation. However, the uncertainty analysis 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 analysis, 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 technology 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 economy improvements (the rebound effect), and the on-road gaps 

between realized fuel economy and laboratory test values for gasoline and diesel vehicles. The 
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shapes and types of the probability distributions used in the analysis vary by uncertainty, though 

the costs and effectiveness values for technologies are sampled as groups to minimize 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 analysis. The sample design of technology

costs (and effectiveness) for the proposal’s Monte Carlo analysis 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 standards), are drawn from a range of fuel prices 

characterized by permutations of the Low, 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 drive 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 ultimately selected to be run in the simulation. In 

particular, the social cost of damages caused by criteria pollutant and greenhouse gas emissions 

have been omitted from the analysis, the latter based on guidance from the interagency working 

group that developed the cost estimates used in the central analysis. 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 by CAFE standards;

(5) The rebound effect;

(6) The on-road gap between achieved real-world fuel economy and the test cycle for 

gasoline and diesel vehicles.

10.2.2.5.2 Technology Costs and Effectiveness
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The costs incurred by manufacturers to modify their 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 analysis. Unlike previous analyses 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 generally 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 generally 

characterized by 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 analysis 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 simultaneously be 

less expensive and more effective (or vice versa) than anticipated in the main analysis for a given 

simulation in this exercise.

10.2.2.5.3 Fuel Prices

For this analysis, fuel prices are sampled as a scaling factor that determines a complete 

time series of prices for all years covered by the analysis. The scaling factor scales the interannual 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, respectively. As EIA 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 prices within that range is as likely 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 apply technology under varying fuel price 

cases by including a variable that represents manufacturers’ assumption about consumer 

willingness-to-pay for fuel economy technology. In this case, “willingness-to-pay” is

characterized as the payback period for fuel economy technology investments, meaning the 

number of years’ worth of fuel savings necessary to balance the cost of the new technology. In 

the main analysis, 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 
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regulatory scenarios. In the case of baseline 1b, only 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 analysis, which uses a single fuel price projection and economic and technological 

parameter values consistent with it, the zero-year and six-month payback assumptions are 

generally consistent with the collection of other assumptions. However, under more extreme fuel 

price scenarios, short payback periods become increasingly unrealistic. 

To address this limitation, NHTSA has included the length of the payback period (that 

manufacturers assume consumers desire) in the uncertainty analysis. Assuming some non-zero 

payback period ensures that when fuel prices are very high, manufacturers will continue to add 

cost-effective fuel economy technologies even when the standards are not sufficient to force 

these additions. As one might expect, higher fuel prices and longer payback periods result in 

more fuel economy technology being added beyond the level mandated by the standards, while 

lower fuel prices and shorter payback periods result in less.

The cases that most challenge internal consistency are naturally found at the extremes, 

low-price-long-payback, for example. In the low-price-long-payback case, the fuel savings 

would still be very low, technology would still not be very attractive, and only a small amount of 

additional fuel economy technology (if any at all) would be added to vehicles already in 

compliance. So one could credibly argue that there is uncertainty about the degree to which 

manufacturers understand consumers’ willingness-to-pay for fuel economy and add slightly more 

than would be demanded.

Similarly, under the high-price-short-payback draws, fuel prices are high enough to make 

some technology additions occur in the baseline, but maybe not the ideal amount under those 

conditions because manufacturers assumed a shorter payback period than consumers have when 

faced with very high fuel prices. Since there is uncertainty about manufacturers’ ability to 

perfectly respond to consumer preferences, these draws produce results that are still plausible (if 

less probable than others). 

The payback period used in the analysis is drawn from a Beta distribution with shape 

parameters equal to 2 and 5. This places about 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 economy and GHG standards are anticipated to result in a slight increase in annual 

miles driven per vehicle. This rebound effect impacts net societal benefits 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 employed a rebound effect of 10 

percent in the main analysis. 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 employed in 
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a slightly skewed Beta distribution which produced a mean of approximately 14.2 percent. The 

difference reflects the more cost-conscious behavior of commercial vehicle operators, 

particularly 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 fuel 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 

typically used for moving people, even when driving style and conditions vary from the test 

procedure, medium-duty vehicles are typically 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 applying 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 types, largely as a result of the different 

sources of discrepancy from the test cycle: city/highway splits, 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 repeatedly 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 empirical distributions for both gasoline and diesel were then fit using 

gamma distributions, and the gamma distributions were sampled in the uncertainty analysis.

10.2.2.5.7 Results

Figure 10-6 displays the distribution of net benefits estimated by the ensemble of 

simulation runs. As Figure 10-6 indicates, the analysis produces a wide distribution of possible 

outcomes that are much broader than the range of estimates characterized by only the difference 

between the more and less dynamic baselines. While the expected value, the probabilityweighted average outcome, is only about 70 percent of the net benefits estimated in the main 

analysis, 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 analysis 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.
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the expected value when uncertainty is considered, net benefits at least as high as those estimated 

in the main analysis 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 payback periods (in years) for Model Year 2029 

trucks across 14,000 simulation runs. The “payback period” typically refers to the number of 

years 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. 
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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 payback distribution (where payback periods are likely to be 

unacceptably long), fewer than ten percent of the trials result in payback periods longer than four 

years. This suggests that, even in the face of uncertainty about future fuel prices and fuel 

economy 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 relatively short period of time.