Appendix G 

Emissions Inventory Methodology and Results 
 
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TABLE OF CONTENTS

TABLE OF CONTENTS .......................................................................................................... I

LIST OF ACRONYMS ...........................................................................................................III

A. INTRODUCTION ....................................................................................................... 1

B. METHODOLOGY ...................................................................................................... 1

C. DATA SOURCES ...................................................................................................... 2

1. Motor Vehicle Registration Data...................................................................... 2

2. International Registration Plan (IRP)............................................................... 3

3. Motor Carrier Permit Program (MCPP) ........................................................... 4

4. International Fuel Tax Agreement (IFTA)........................................................ 4

5. Vehicle Inventory and Use Survey (VIUS) ...................................................... 5

6. ARB Vehicle Surveys ...................................................................................... 5

7. Estimated Emission Rates .............................................................................. 5

8. UC Davis Out-of-State Truck Travel Surveys.................................................. 5

D. BASE YEAR POPULATION AND ACTIVITY BY AGE ........................................................ 6

1. Utility Fleets..................................................................................................... 8

2. Drayage Trucks Serving California’s Ports and Railyards............................... 9

3. Trucks Serving the Agricultural Economic Sector ......................................... 14

4. Medium Heavy Duty Diesel Trucks ............................................................... 18

5. California Registered Heavy Heavy Duty Diesel Trucks ............................... 23

6. Out-of-State Heavy Heavy Duty Diesel Trucks ............................................. 28

7. Buses ............................................................................................................ 33

8. Power Take Off Operations in California....................................................... 35

E. EMISSION RATES .................................................................................................. 36

1. Updated MHDDT Emissions ......................................................................... 36

2. Revised engine market information............................................................... 38

3. Carbon Dioxide (CO2) Emission Rates.......................................................... 40

4. Final emissions rates .................................................................................... 44

F. FORECASTING THE BASELINE INVENTORY ............................................................... 44

1. Growth........................................................................................................... 44

2. Attrition.......................................................................................................... 46

3. Pre-buy.......................................................................................................... 47

G. STATEWIDE BASELINE ACTIVITY AND EMISSIONS...................................................... 53

1. Base Year Age Distributions across Inventory Category............................... 54

2. Base Year and Forecasted Population by Inventory Category...................... 56

3. Comparing Accrual Rates by Inventory Category ......................................... 57

4. Base Year and Forecasted Vehicle Miles Traveled by Inventory Category... 58
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5. Statewide Baseline Emissions Estimates...................................................... 62

H. STATEWIDE TRUCK AND BUS REGULATION BENEFITS............................................... 68

1. Methodology to Assess Statewide Benefits................................................... 68

2. Statewide Benefits ........................................................................................ 76

I. REFERENCES........................................................................................................ 89
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List of Acronyms

ARB Air Resources Board 

BACT Best Available Control Technology 

BOE Board of Equalization 

BTS Bureau of Transportation Statistics 

CHP California Highway Patrol 

CRC Coordinating Research Council 

CY Calendar Year 

DMV Department of Motor Vehicles 

DOF Department of Finance 

DPF Diesel Particulate Filter 

DR Deterioration Rate 

ECE Electronically Controlled Engines 

EGR Exhaust Gas Recirculation 

FE Fuel Economy 

GDP Gross Domestic Product 

GVWR Gross Vehicle Weight Rating 

HH Heavy Heavy (Duty Diesel Trucks) 

HHDDT Heavy Heavy Duty Diesel Trucks 

IFTA International Fuel Tax Agreement 

IRP International Registration Plan 

LESBP Lower Emissions School Bus Program 

LA/LB Los Angeles/Long Beach 

MCPP Motor Carrier Permit Program 

MH Medium Heavy (Duty Diesel Trucks) 

MHDDT Medium Heavy Duty Diesel Trucks 

MY Model Year 

NOOS Neighboring out-of-state 

NNOOS Non-neighboring out-of-state 

PM Particulate Matter 

POAK Port of Oakland 

POLA/LB Ports of Los Angeles and Long Beach 

PTO Power Take Off 

SCR Selective catalytic reduction 

TIAX TIAX LLC Consultants 

USDOE United States Department of Energy 

USEPA United States Environmental Protection Agency

VIN Vehicle Identification Number 

VIUS Vehicle Inventory and Use Survey 

VMT Vehicle Miles Traveled 

ZMR Zero-mile rate 
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A. Introduction 

Commercial heavy-duty diesel trucks and buses (defined as commercial diesel buses 

and trucks exceeding 14,000 lbs gross vehicle weight rating (GVWR)) are currently the 

single largest source of nitrogen oxide (NOx) emissions in California, accounting for 

30% of statewide NOx emissions (ARB, 2008). These same trucks buses are also the 

largest source of diesel particulate matter (diesel PM) in California, representing about 

40% of statewide diesel PM emissions. 

On-road mobile source emissions in California are currently calculated using the ARB’s 

EMFAC2007 model that was released in December of 2006 (ARB, 2006). Since the 

last EMFAC release, Staff members have conducted a comprehensive re-evaluation of 

the heavy duty diesel truck emissions inventory. In developing this new analysis, we 

have integrated new data and assumptions into an expanded methodology that builds 

upon current modeling in EMFAC2007. With this methodology, we incorporate detail for 

different types of trucking operations and truck configurations that are referred to as 

“inventory categories”. Emission factors differ from those in EMFAC2007 and reflect 

our enhanced knowledge of trucking operations in California that has been developed 

through this effort. With this document we describe our approach and the results from 

its application. With this document we also provide emissions estimates for each 

category of vehicles that would be regulated under the proposed Statewide Truck and 

Bus Regulation. 

B. Methodology 

Fundamentally the EMFAC model uses a simple, vehicle population-based technique 

for estimating emissions for any type of on-road vehicle. We calculate emissions as the 

product of a population of vehicles, the number of miles traveled per vehicle, and 

emission rates for each vehicle per mile. Beneath this simple equation lies a series of 

data and assumptions about the population, miles traveled, and emission rates per 

vehicle model year in a given calendar year, growth and attrition estimates, deterioration 

rates, and other factors that affect emissions estimates. 

Our revised approach for estimating commercial heavy-duty diesel truck emissions 

builds upon this concept by applying it separately for each category of trucks. These 

categories were selected by evaluating different groups of trucks that have similar 

travel, service, size, age or other characteristics within the category but differing 

between categories. 
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The methodology used to develop the proposed rule inventory is based on the following 

equation: 

 EMSCY = ∑MY, C (POPMY, C X AC MY, C X ER MY, C)

where: EMSCY is the emissions calculated in tons per day for a given calendar year 

CY. 

 POPMY, C is the population of trucks for model year MY within each inventory 

category C for a given calendar year; 

• ACMY, C is the accrual rate (miles traveled per year) per truck by model year 

MY and inventory category C in a given calendar year; 

• ERMY, C is the calculated emission rate, in grams pollutant per mile driven, 

assuming statewide speed travel distributions in EMFAC2007 and 

category-specific cumulative mileage accrual over the life of the truck, by 

model year MY and inventory category C; 

With this new analysis, we developed a population and model year distribution for each 

vehicle category. We also estimated accrual by model year for the category and 

cumulative mileage accrual (odometer) by model year. Because trucks can move 

between categories as they age, we assessed the movement of used trucks between 

categories in order to develop cumulative mileage accrual estimates that reflect this 

movement. As a result, cumulative odometer readings by model year will not 

necessarily be consistent with accrual schedules for each inventory category. We 

developed emission rates using EMFAC2007 and statewide speed distributions, and we 

adjusted emission rates for modeled odometer readings by category. A more complete 

discussion of the data sources used is provided in the following section. 

C. Data Sources 

We have used many different data sources to develop input data to the methodology 

described above. In this section we provide a general description of each data source 

as well as links for further information. 

1. Motor Vehicle Registration Data 

The California Department of Motor Vehicles (DMV) is responsible for the process of 

vehicle registration in California (DMV, 2001). As part of this program, commercial 

trucks and buses are required to pay registration annually; however, vehicle owners 

have the option of registering their vehicles on a seasonal basis. Staff have an 

agreement with the DMV to get registration data downloads in April and October of each 

year. Staff process the data in order to compile a list of vehicles by vehicle class, body 

type, rated weight, and other parameters. 

Using each vehicle’s license plate configuration, which separates International 

Registration Plan (IRP) vehicles from non-IRP vehicles, each vehicle’s rated weight, 

and each vehicle’s body type data, we separated vehicles into different inventory 

categories. As such DMV registration was the primary source for vehicle population 
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and model year by category. We also used the DMV registration data to group trucks 

by fleet and thus fleet size. With these data we could assess the fraction of owneroperators in each category. We did this by sorting data by owner/operator name and 

address before grouping trucks into trucking categories. Overall, owner-operator trucks 

tend to be older than other trucks within the same inventory category. These results will 

be discussed in detail in subsequent sections of this report describing each inventory 

category. 

2. International Registration Plan (IRP) 

The International Registration Plan is a program administered by the American 

Association of Motor Vehicle Administrators to transfer registration fees assessed to 

commercial and other vehicles that cross state boundaries, in accordance with the 

number of miles affected vehicles travel in each participating U.S. State or Canadian 

Province (IRP Inc., 2008). IRP-registered vehicles most often include commercial 

heavy-duty trucks and buses but can also include government vehicles and smaller 

vehicles. Under this program a fleet of vehicles has the option of registering their 

vehicles in any state where a portion of their fleet is domiciled. 

IRP recordkeeping varies by state with some states maintaining electronic databases 

and others paper files. To facilitate data analysis, Staff obtained IRP data from states in 

electronic format whenever possible. Staff received in electronic format comprehensive 

reports representing mileage driven by California-registered IRP trucks in calendar 

years 2000-2006, and obtained hardcopy samples of IRP data from a number of other 

jurisdictions for miles driven by their IRP trucks in California. 

IRP data representing California trucks were used to directly assess the population and 

model year distribution of these trucks, as well as the fraction of IRP-registered vehicles’ 

mileage accrual that occurs within California. To evaluate out-of-state IRP data, we 

copied and analyzed one month’s worth of IRP updates by fleet. This represents about 

9% of all fleets operating in a given state because a similar number of fleets are 

required to report each month. Data were collected from four neighboring states 

(Arizona, Idaho, Nevada, Oregon) as well as eight non-neighboring states (Alabama, 

Indiana, Nebraska, New Jersey, Oklahoma, Tennessee, Texas, and Wisconsin). Of 

these states, Alabama, Nebraska, New Jersey, and Oklahoma each provided a model 

year or Vehicle Identification Number (VIN) for each truck in each fleet, allowing detailed 

age distributions for each of these states to be developed. Staff relied on roadside 

survey data to develop age distributions for other states. 

All IRP data are provided at the level of a fleet rather than the level of an individual 

vehicle. Each fleet registered with the IRP reports the total number of power units or 

trucks in the fleet, the total miles traveled by that fleet, and the total miles traveled in 

California by that fleet. Reporting is completed annually. Many fleets reporting to IRP 

are large; as a result, the population of trucks reflected in that fleet’s report will reflect a 

large number of trucks that do not enter California even though they may be authorized 

to do so. In addition, each state’s data format is different; some states provide 

information such as VIN or model year for each truck in the fleet, while other states 
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don’t. Where model year data were available, staff used the information directly. 

Where model year data were not available but VIN data were, staff decoded the VIN to 

derive model year. Staff also received a summary report from DMV that provided the 

total population of trucks in the IRP program from each state. This report provided an 

estimate of the number of trucks in fleets cleared to come into California from other 

states. 

3. Motor Carrier Permit Program (MCPP) 

The California Department of Motor Vehicles and California Highway Patrol (CHP) 

jointly administer the Motor Carrier Permit Program (MCPP), which applies to any 

operator of a commercial vehicle in California exceeding 10,000 lbs GVWR or truck and 

trailer combinations exceeding 40 feet in length. The program generally applies to all 

commercial vehicle operators with an office in California. MCPP data provide 

information on the number of vehicles per fleet and the number of fleets operating in 

California; the data are separated between for-hire and private carriers. The database 

does not provide license plate, VIN or GVWR of trucks in each fleet. 

We obtained the DMV/CHP 2005 database and used it to estimate the fraction of truck 

owner-operators in California. These data were compared to similar estimates derived 

from DMV registration data. Because the MCPP database does not provide information 

on individual truck size, activity, or model year, we ultimately chose to use DMV 

registration data to assess the fraction of total owners that are owner-operators. 

4. International Fuel Tax Agreement (IFTA) 

The International Fuel Tax Agreement (IFTA) is an agreement among U.S. states and 

Canadian provinces to simplify the reporting of fuel use taxes by interstate motor 

carriers. In California, IFTA is administered by the Motor Carrier Section of the Board of 

Equalization (BOE) (BOE, 2008). The program operates similarly to IRP in that motor 

carriers may choose a state for filing fuel tax returns and then each state distributes fuel 

taxes among other states depending on the fraction of fuel burned and miles traveled by 

each fleet in each jurisdiction. 

Staff obtained 2005 aggregated IFTA data as well as 2006 and 2007 IFTA data 

summarized for each state by the California BOE. For each state, BOE provided 

information on the number of miles traveled and the amount of fuel burned within 

California by trucks from each reported state. However, data for 2006 and 2007 were 

incomplete with about 10% of the data in each of those years not summarized. For 

states with incomplete data, we extrapolated estimates using overall population data 

from IRP. Staff used the 2005 IFTA aggregated out-of-state records to estimate the 

miles traveled in California by out-of-state heavy-heavy duty trucks; Staff used the 2006 

and 2007 data to develop the ratio between trucks from neighboring and nonneighboring states. 
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5. Vehicle Inventory and Use Survey (VIUS) 

The main intent of the Vehicle Inventory and Use Survey (VIUS) is to provide data on 

the physical and operational characteristics of the nation's private and commercial truck 

population (US Census Bureau, 2002), including national and state-level estimates of 

the total number of trucks. Up until 2002, this survey was conducted every 5 years by 

the U.S. Census Bureau as part of the national census but VIUS is no longer being 

updated due to lack of funding. Nevertheless, VIUS provides a wealth of information on 

truck body types, mileage accrual, odometer, and many other factors. 

We used VIUS data reported by trucks operating in California to develop mileage 

accrual rates and cumulative odometer by model year for interstate trucks and by model 

year and body type for California-based trucks. 

6. ARB Vehicle Surveys 

As part of this rule development, staff developed an on-line survey for truck and bus 

fleets in general (ARB, 2008), as well as industry-specific surveys of individual truck 

categories including agricultural trucks, dump trucks, and others. These surveys 

provided information such as truck age, miles traveled, body type, and other factors 

useful for inventory development. 

7. Estimated Emission Rates 

The EMFAC2007 model (ARB, 2006a and 2006b) was used as the starting point for 

developing emission rates used in this inventory. EMFAC2007 emission rates are 

based on analysis of chassis dynamometer testing conducted by the Coordinating 

Research Council (CRC) under the E55/59 testing program (CRC, 2007). Although 

both heavy-heavy and medium-heavy duty trucks were tested in the E55/59 study, only 

heavy-heavy duty truck emission rates were updated for EMFAC2007. 

As part of this rule development, staff made three revisions to EMFAC emission rates. 

Medium-heavy duty truck emission rates were updated with new data made available 

through the CRC E55/59 program. Staff re-evaluated assumed penetration rates of 

new technologies into truck sales between 2006 and 2011, and assessed the emissions 

impact of these revised assumptions. Carbon dioxide emission rates were updated 

based upon new analysis of several different data sources. Each of these revisions is 

discussed further in this document. We anticipate incorporating these revised emission 

rates into the next EMFAC update scheduled for 2010. 

8. UC Davis Out-of-State Truck Travel Surveys 

In 2006 the ARB contracted with researchers at the University of California at Davis 

(UC-Davis) to develop and administer truck surveys at major border crossings into 

California from Oregon, Nevada, Arizona, and Mexico (Lutsey, 2008). For these 

surveys, interstate trucks were assumed to be those trucks that were registered, 

domiciled, and/or refueled outside California. Researchers administered 433 surveys of 

truck drivers at seven weigh stations near state borders with high commercial truck 
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traffic (Lutsey, 2008). These surveys provided estimates of annual travel activity in 

California for each of the respondents, allowing staff to estimate the number of out-ofstate trucks, their mileage, fuel usage, and fueling locations. Based on results of this 

study, staff examined whether estimates of interstate truck age, mileage, and fuel usage 

assumed in EMFAC2007 were reasonable. 

Staff also used raw data from this analysis to validate estimates of model year 

distribution for out-of-state trucks that were developed using IRP data. In an 

accompanying report, UC Davis estimated the fraction of total VMT in California 

represented by out-of-state trucks relative to EMFAC2007. ARB did not use these 

estimates because, subsequent to the Lutsey (2008) report being finalized, staff 

received IFTA data from the BOE that provided direct mileage reports by state of 

registration. These issues will be described in greater detail later in this document. 

D. Base Year Population and Activity by Age 

In Table 1 below, we provide a list of the data sources underlying each of the truck 

inventory categories to be discussed in the next section including how each data source 

was used to help develop truck population and activity estimates. 

Table 1. Data Sources Used to Develop Population and Activity Estimates 

Type / 

Category Population Activity 

Heavy-Heavy 

/ Out-of-State 

CA Dept. of Motor Vehicles (DMV) International 

Registration Plan (IRP) reports; adjusted to 

account only for trucks that enter California. 

Model-year distribution from IRP data and 

surveys. Category split between neighboring 

states (WA, OR, NV, AZ, ID) vs non-neighboring 

states. 

Vehicle Inventory and Use Survey (2002) - 

Fraction of mileage accrued in California 

estimated using IRP data samples and 

International Fuel Tax Agreement Data (IFTA) 

Heavy-Heavy 

/ CaliforniaInterstate 

DMV CA-IRP reports provide population by 

model year 

VIUS for nationally registered trucks in IRP as 

above. CA-IRP reports fraction of mileage 

accrued in CA, and IFTA reports for total 

mileage. 

Heavy-Heavy 

/ In-State 

Tractor 

DMV Registration data, adjusted to subtract 

vehicles from specific categories such as Utility, 

Drayage, and others. 

VIUS 2002 data 

Heavy-Heavy 

/ In-State 

Single 

DMV Registration data, adjusted to subtract 

specific vehicles from specific categories such as 

Utility, Drayage, and others. 

VIUS 2002 data 

Heavy-Heavy 

/ Drayage 

Tractors 

License plate and gate count surveys conducted 

at the Ports of Los Angeles, Long Beach, and 

Oakland. 

Trip-based model developed for ARB Drayage 

Truck Regulation; mileage accrual assumed flat 

at total mileage divided by total number of 

trucks. 

Heavy-Heavy 

/ Agricultural 

Trucks 

Age distribution from survey; population 

extrapolated from survey results; specialty trucks 

estimated from survey and registration data. 

Accrual from survey 

Heavy-Heavy 

/ Utility 

Trucks 

Population and age of trucks registered to public 

utilities in DMV database. Surveys conducted for ARB Public Fleet Rule. 
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Type / 

Category Population Activity 

MediumHeavy / InState Trucks 

DMV registration data VIUS 2002 data 

MediumHeavy / 

Interstate 

Trucks 

IRP reports VIUS 2002 data 

MediumHeavy / 

Agricultural 

Trucks 

Age distribution from survey; population 

extrapolated from survey results; specialty trucks 

estimated from survey and registration data. 

Accrual from survey 

MediumHeavy / Utility 

Trucks 

DMV registration data. Surveys conducted for ARB Public Fleet Rule. 

Buses / 

School California Highway Patrol Data ARB Surveys 

Buses / Other EMFAC2007 EMFAC2007 

Other / Power 

Take-Off 

No population estimated. Total fuel usage 

provided by California State Board of 

Equalization; age distribution assumed same as 

in-state single-unit trucks 

Fuel usage converted to equivalent mileage 

assuming EMFAC speed distributions and fuel 

economy. 

One of the key assumptions in the development of this inventory is that trucks and 

buses that are grouped by vocation or body type have a common age distribution and 

accrual schedule. This assumption has been verified through analysis of the data 

sources described above. 

Another assumption that applies to many truck inventory categories is that trucks 

typically move between categories as they age and accrue mileage. Staff recognized 

this through analysis of DMV data. For example, heavy-heavy duty diesel trucks 

(HHDDTs) that are used for interstate travel (e.g. by CA IRPs) are often retired from 

interstate travel after a few years due to increasing maintenance costs and bought for 

intrastate travel where reliability is less of a concern. Vehicles are driven many more 

miles during interstate travel than during intrastate travel. As a result, a ten year old 

truck that was used during its first five years for interstate travel and its last five for 

intrastate travel would, on average, have a higher odometer reading than a vehicle used 

strictly for intrastate travel during those ten years. Staff used estimated as to when 

vehicles would most typically be transferred between inventory categories and adjusted 

the odometer readings from those assumed in EMFAC2007 appropriately. 

This section describes each vehicle category including the key assumptions and data 

analysis results that underlie the development of the inventory. For each category we 

show the age distribution in calendar year 2008 as well as the anticipated mileage 

accrual of vehicles of different age. We developed age distributions for trucks ranging 

between ages -1 and 44. Trucks of age -1 represent vehicles sold and operated in the 

calendar year prior to the model year (e.g. MY2009 trucks sold in CY2008). Model 

years beyond age 44 were included in the 44 age bin since the EMFAC model handles 

only 45 model years. We also show the distribution of trucks in each category between 
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different fleet sizes where appropriate and the difference in the average age of vehicles 

between different-size fleets. 

1. Utility Fleets 

In October 2006, the ARB adopted a regulation designed to reduce emissions from 

diesel trucks that are owned or operated by private utilities that operate in California 

(ARB, 2006). The utility truck category as defined by the regulation includes both 

medium-heavy (14,000 – 33,000 lbs gross vehicle weight rating or GVWR - MHDDT) 

and heavy-heavy (>33,000 lbs GVWR - HHDDT) trucks but does not include refuse 

haulers or fire trucks and other emergency vehicles operated by public agencies. The 

regulation required fleet operators to reduce diesel PM emissions on a defined schedule 

by purchasing newer regulation-compliant engines or installing diesel particulate filters. 

The following methodology was used to develop a Utility Fleet emissions inventory. 

a) Base Year Population and Age Distribution by Fleet Size 

Staff used an identical methodology for assessing the utility truck population and model 

year distribution as was used in development of the utility fleet regulation (ARB, 2005). 

Staff developed a list of utility names and used those names to extract vehicles from 

DMV registration data. Those vehicles were then separated by model year and weight 

category (MHDDT vs HHDDT). The population-weighted average age of a vehicle in 

the utility fleet category was estimated at 8.2 years for HHDDT, 7.2 years for MHDDT. 

Figure 1 provides the distribution of the California utility truck population by age for 

calendar year 2008. 

Figure 1: Utility Truck Population and Model Year Distribution (2008) 

0

50

100

150

200

250

300

350

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

Utility MH Utility HH
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b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds 

Staff used accrual rates and cumulative odometer readings that were published in the 

utility fleet rule staff report (ARB, 2005). These were developed by TIAX (2003); results 

showed that utility trucks accrue between 6,000 and 8,000 miles per year, with slightly 

more miles accrued per year by HHDDT. The estimated annual average mileage 

accrual was 7,800 miles per year for HHDDT and 6,200 miles per year for MHDDT, all 

of which is assumed to occur in California. The estimated average odometer reading 

was 122,000 miles for HHDDT and 78,000 miles for MHDDT. In Figure 2 we plot 

annual accrual and cumulative modeled odometer readings for utility category trucks. 

 

Figure 2: Utility Truck Category Accrual and Modeled Odometer Readings (2008) 

0

2,000

4,000

6,000

8,000

10,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Accrual (miles/yr)

0

100,000

200,000

300,000

400,000

500,000

Odometer (miles)

Utility MH Accrual Utility HH Accrual

Utility MH Odometer Utility HH Odometer

c) Base Year Vehicle Miles Traveled by Fleet Size and Mileage 

Thresholds 

Utility fleet vehicles were not separated into different categories by fleet size or mileage 

threshold. This vehicle and emissions inventory evaluates only the fleets of privately 

owned utilities. The utility fleets of larger municipal agencies are not included in this 

assessment. 

2. Drayage Trucks Serving California’s Ports and Railyards 

In 2007 the ARB passed a regulation requiring NOx and diesel PM emissions reductions 

from drayage trucks serving California’s ports and railyards (ARB, 2007a). Drayage 

trucks are typically older than other trucks in California and primarily serve the Ports of 

Los Angeles, Long Beach and Oakland along with railyards near those ports. Drayage 
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truck travel is predominantly regional; in general, these trucks make multiple trips to and 

from the facilities each week. Drayage trucks are all heavy-heavy duty vehicles, 

exceeding 33,000 lbs GVWR. According to the regulation, by calendar year 2010 all 

drayage trucks must meet at a minimum model year 1994 or later emissions standards 

with a diesel particulate filter and by 2014 all trucks must meet 2007 truck emission 

standards. The following methodology was used to develop the drayage truck 

emissions inventory: 

a) Base Year Population and Age Distribution by Fleet Size 

Drayage trucks are defined by their operation rather than body type. Since DMV 

registration records provide information on body type but not vehicle operator, DMV 

registration cannot be used alone to estimate the statewide drayage truck population. 

The population of trucks serving the Ports of Los Angeles and Long Beach and 

associated railyards was derived from an analysis of one year of gate count and license 

plate information from approximately half of the terminals at the two Ports; these data 

were provided by officials from the ports. Staff compared observed license plates to 

DMV data to assess model year distribution; we then extrapolated results to all 

terminals at both Ports. A similar approach was used for the Port of Oakland and 

associated railyards. Regulatory documentation (ARB, 2007b) describes in more detail 

the methodology used to assess the population of drayage trucks. We estimated the 

age of drayage trucks serving other ports and railyards in California by assuming that 

these trucks were similar to those in the HHDDT (instate) tractor category (ARB, 

2007b). The population-weighted average age of a vehicle in the drayage category was 

estimated at 12.4 years for trucks serving the Ports of Los Angeles and Long Beach and 

regional intermodal railyards and 9.7 years for drayage trucks serving Oakland and 

associated railyards and for trucks serving the remainder of California ports. In Figure 3 

we provide drayage truck population by model year for calendar year 2008. As shown, 

trucks serving the Ports of Los Angeles and Long Beach are typically several years 

older than drayage trucks serving other California ports. 
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Figure 3. Drayage Truck Population and Model Year Distribution (2008) 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

LA/LB Region Bay Area Other ports
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b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds 

Staff used accrual rates and cumulative odometer readings published in the drayage 

truck rule staff report (ARB, 2007b) to estimate the vehicle miles traveled (VMT) 

associated with drayage trucks in each region. Staff then assumed that accrual rates 

were independent of age for drayage trucks and that the vehicle population increased in 

proportion to the overall VMT for the inventory category. 

Drayage trucks serving the Ports of Los Angeles and Long Beach were estimated to 

drive around 41,000 miles per year. Drayage trucks serving the Port of Oakland drive, 

on average, around 40,000 miles per year. Drayage trucks serving other ports in 

California are assumed to have travel characteristics similar to in-state HHDDT tractors 

and therefore drive, on average, 49,000 miles per year. All drayage-related VMT are 

assumed to accrue in California. Odometer readings are modeled assuming that 

drayage trucks were purchased used from larger national fleets that drive hundreds of 

thousands of miles in their first several years of operation. This assumption is 

described in detail in the in-state tractor category. The resulting average odometer 

reading as estimated was 860,000 miles for LA/LB trucks and 770,000 miles for trucks 

serving Oakland and other ports/railyards; the apparent difference from the mileage if 

projected from the accrual rate and the age results from the trucks likely having been 

used for other purposes before being converted to drayage trucks. In Figure 4 we plot 

annual accrual and cumulative modeled odometer readings for drayage trucks. 

Figure 4. Drayage Truck Category Accrual and Modeled Odometer Readings 

(2008) 

0

20,000

40,000

60,000

80,000

100,000

-1 1 3 5 7 9 1113151719212325272931333537394143

Age

Accrual (miles/yr)

0

400,000

800,000

1,200,000

1,600,000

2,000,000

Odometer (miles)

LA/LB Accrual Oakland Accrual Other Ports Accrual

LA/LB Odometer Oakland Odometer Other Ports Odometer
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c) Base Year Vehicle Miles Traveled by Fleet Size and Mileage 

Thresholds 

Drayage trucks were categorized by fleet size as single truck fleets, two truck fleets, 

three truck fleets, and fleets of more than three trucks. No differentiation was made for 

drayage trucks with regard to mileage threshold, on the assumption that nearly all 

drayage fleets are operated in a similar manner. In Table 2, Table 3, and Table 4 we 

show the distribution of VMT among drayage fleets of various fleet sizes and VMTweighted ages for trucks near the Ports of Los Angeles/Long Beach, Oakland, and all 

other California ports/railyards, respectively. The age distribution of drayage trucks was 

also assumed not to differ with regard to fleet size. With regard to the VMT-weighted 

average age within each category, drayage trucks near LA/LB were estimated to be 

older, on average, than drayage trucks near other facilities. In Table 5 we provide a 

summary of the VMT driven by different size drayage trucks fleets serving all California 

facilities. 

Table 2. Distribution of VMT among POLA/LB Drayage Trucks by Fleet Size 

(2008) 

Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 1,219,969 53.7% 12.4 

2 trucks 135,552 6.0% 12.4 

3 trucks 152,496 6.7% 12.4 

> 3 trucks 762,481 33.6% 12.4 

Total 2,270,498 100.0% 

Table 3. Distribution of VMT among Port of Oakland Drayage Trucks by Fleet Size 

(2008) 

Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 210,556 53.7% 9.7 

2 trucks 23,395 6.0% 9.7 

3 trucks 26,319 6.7% 9.7 

> 3 trucks 131,597 33.6% 9.7 

Total 391,868 100.0% 

Table 4. Distribution of VMT among Drayage Trucks at Other Ports/Railyards by 

Fleet Size (2008) 

Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 127,097 53.7% 9.1 

2 trucks 14,122 6.0% 9.1 

3 trucks 15,887 6.7% 9.1 

> 3 trucks 79,436 33.6% 9.1 

Total 236,542 100.0% 
G-14 

Table 5. Distribution of VMT among Drayage Trucks at all California 

Ports/Railyards by Fleet Size (2008) 

Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 1,557,622 53.7% 11.8 

2 trucks 173,069 6.0% 11.8 

3 trucks 194,703 6.7% 11.8 

> 3 trucks 973,513 33.6% 11.8 

Total 2,898,907 100.0% 

3. Trucks Serving the Agricultural Economic Sector 

Agricultural trade associations, in conjunction with staff, administered a survey to 

farmers, ranchers, and other agricultural businesses designed to identify and 

characterize trucks associated with agricultural businesses. The stakeholder survey 

was designed primarily to capture trucks owned and operated by farms, ranches, and 

first processing facilities, and was similar in format to the on-line survey used to collect 

information on the broader truck fleet as part of this rule development (ARB, 2008). 

Results from the survey were used to assess the population of MHDDT and HHDDT 

that operate in California for agricultural purposes, either seasonally or annually. The 

survey was also used to assess the model year distribution and mileage accrual of 

these trucks, as well as the fraction that would be captured by proposed rule 

exemptions. 

a) Base Year Population and Age Distribution by Fleet Size 

To assess the model year distribution of agricultural trucks, we compiled survey results 

for MHDDT and HHDDT separately, and modeled the data in order to smooth trends in 

model year variability. Based on analysis of the survey data, the population-weighted 

average ages of HHDD and MHHD agricultural trucks were found to be very similar 

(17.3 years for HHDDTs and 17.2 years for MHDDTs). 

To assess population, we compiled the survey results and extrapolated the survey 

sample to a statewide population using the numbers of acres farmed and other metrics 

collected in the survey as scaling factors. Using this methodology, Staff estimated a 

statewide agricultural truck population of between 40,000 and 60,000 vehicles. 

However, when staff compared the model year specific truck populations estimated 

using the extrapolated data to model year specific DMV registration data, they found 

that the extrapolated agricultural truck populations for certain model years exceeded the 

total number of trucks of that model year in the DMV database. This suggested that the 

directly extrapolated survey results were overestimating the agricultural truck 

population, at least for those model years. 

In reviewing the surveys, staff found that very few were administered to farms or 

organizations that do not own agricultural trucks. In effect, these businesses were 

excluded from the survey; thus, extrapolating the population using the metrics described 

above would overestimate the agricultural truck population. 
G-15 

To correct for this overestimation, we used the DMV registration data as an upper 

bound of the possible number of agricultural trucks of a given model year. 

Using the statewide truck population, we back-calculated an agricultural truck 

population assuming that the agricultural truck population, in any model year, could not 

exceed 80% of the total trucks registered in that model year. With this technique, we 

assessed a likely population of 22,150 agricultural trucks in California, of which 45% are 

MHDDT and 55% are HHDDT. In Figure 5 we show the California agricultural truck 

population and model year distribution estimated for the 2008 calendar year. 

Figure 5. Agricultural Truck Population and Model Year Distribution (2008) 

0

100

200

300

400

500

600

700

800

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

Ag Trucks MH Ag Trucks HH

b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds 

We analyzed survey results in order to estimate mileage accrual for agricultural trucks. 

Because agricultural HHDDT are assumed to be purchased used, their modeled 

odometer follows a composite that accounts for the mix of single-unit and combination 

trucks entering the category as well as the likelihood that most tractors currently in the 

agricultural category were previously in other categories. Because in-state tractors and 

single-unit trucks are driven more, on average, than agricultural trucks, the composite 

odometer reading is projected to be higher than the odometer reading of a truck that 

had been used exclusively for agriculture. These trucks then follow the agricultural 

truck accrual rate upon transition. Agricultural MHDDT also followed a composite rate, 

since in-state MHDDT were also assumed to migrate toward the agricultural category. 

The average odometer reading for agricultural was estimated to be approximately 
G-16 

601,000 miles for HHDDT and 293,000 miles for MHDDT. The average annual mileage 

accrual was estimated to be 23,000 miles for HHDDT agricultural trucks and 11,000 

miles for MHDDT agricultural trucks. All miles are assumed to accrue in California. We 

present the results in Figure 6. 

Figure 6. Agricultural Truck Category Mileage Accrual and Modeled Odometer 

(2008) 

0

10,000

20,000

30,000

40,000

50,000

60,000

-1 1 3 5 7 9 1113151719212325272931333537394143

Age

Accrual (miles/yr)

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

Odometer (miles)

MH Ag Trucks Accrual HH Ag Trucks Accrual

MH Ag Trucks Odometer HH Ag Trucks Odometer

c) Base Year Vehicle Miles Traveled by Size and Mileage Thresholds 

Staff did not categorize agricultural trucks by fleet size. With regard to application, both 

medium-heavy and heavy-heavy trucks were categorized into specialty and nonspecialty vehicles. Specialty vehicles are specifically defined under the proposed 

regulation as water trucks used on the farm, nurse rigs, cotton module movers, or feed 

or mixer feed trucks owned by a cattle or calf feedlot. Using this definition, by analyzing 

the DMV registration data, we found that 10% of agricultural trucks or approximately 

2200 agricultural trucks statewide were specialty vehicles. 
G-17 

Non-specialty agricultural trucks were categorized according to mileage thresholds, 

since the regulation is to be applied differently to vehicles driven different mileage 

thresholds each year. The regulation sets the low use mileage threshold for trucks in 

general at 10,000 miles per year. Vehicles that exceed 10,000 miles per year but do 

not exceed an upper mileage threshold need to turnover or retrofit their trucks prior to 

2017. Agricultural trucks are below the upper mileage threshold if they fall into one of 

the following three categories: 

 MY 1995 or earlier, and driven less than 15,000 miles/year 

 MY 1996-2005, and driven less than 20,000 miles/year, or 

 MY 2006 or newer, and driven less than 25,000 miles/year 

These categories of agricultural truck are subject to regulatory requirements at the 

beginning of calendar year 2017 and are categorized as “between mileage thresholds”. 

The third category consists of trucks that exceed the upper VMT threshold; these trucks 

are subject to the ARB regulation according to the standard turnover and retrofit 

timeline. 

In Table 6 we show the distribution of daily VMT among medium-heavy agricultural 

trucks in California in calendar year 2008. In Table 7 we show the distribution of daily 

VMT among heavy-heavy agricultural trucks in California in 2008. In each table we also 

show the average VMT-weighted age by mileage threshold. For both heavy-heavy and 

medium-heavy agriculture truck weight classes, the trucks driven the fewest miles 

tended to be older than other vehicles. 

Table 6. Distribution of VMT among Medium-Heavy Agricultural Trucks by Fleet 

Size (2008) 

Vehicle Type Daily VMT Share VMT-weighted

Average Age 

Non specialty, below lower threshold 46,021 13.4% 16.0 

Non specialty, between thresholds 78,768 23.0% 10.3

Non specialty, above upper threshold 183,598 53.6% 9.2 

Ag specialty vehicle 34,265 10.0% 10.5 

Total 342,652 100.0% 

Table 7. Distribution of VMT among Heavy-Heavy Agricultural Trucks by Fleet 

Size (2008) 

Vehicle Type Daily VMT Share VMT-weighted

Average Age 

Non specialty, below lower threshold 82,489 9.4% 17.1 

Non specialty, between thresholds 62,773 7.1% 12.5 

Non specialty, above upper threshold 645,375 73.5% 10.5 

Ag specialty vehicle 87,849 10.0% 11.4 

Total 878,486 100.0% 
G-18 

4. Medium Heavy Duty Diesel Trucks 

Staff estimate that more than 200,000 MHDDT are registered in and operate in 

California. These trucks are primarily lighter weight delivery trucks with a GVWR 

between 14,000 and 33,000 lbs that travel during the work day within the area where 

they are registered. Most of these trucks are registered in and never leave California, 

although a few are California-registered and in the IRP program, and a few enter 

California from other states. Most MHDDTs are found in fleets of two or more although 

some are single-truck-fleet. 
G-19 

a) Base Year Population and Age Distribution by Fleet Size 

Staff identified MHDDT in the DMV registration database by selecting all diesel powered 

vehicles with a GVWR between 14,000 and 33,000 lbs. The data were then analyzed in 

two ways. First, staff sorted the registration data by owner name and address to identify 

the number of vehicle records associated with a unique owner. This list was then used 

to develop the fleet size distribution, including the number of owner-operators and small 

fleets. Staff then used a combination of license plate and IRP registration data to 

estimate how many of the MHDDT are registered in California compared to other states, 

as well as what fraction of their travel is solely within California. Staff found that the vast 

majority of MHDDT that are registered in California never leave California. Also, using 

IRP data, staff estimated that very few out-of-state trucks fall into the MHDDT category. 

Since both the public and agricultural truck categories include MHDDT, we subtracted 

these by model year from the in-state MHDDT category to avoid double counting. We 

estimated the population-weighted average age of an out-of-state or IRP MHDDT at 5.4 

years as compared to 8.0 years for an instate MHDDT. In Figure 9 we show the 

population and age distribution estimated for MHDDTs operating in California. 

Figure 7. MHDDT Population and Model Year Distribution (2008) 

0

5,000

10,000

15,000

20,000

25,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

MH Instate MH Out of state MH CAIRP

b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds 

Staff used the most recent VIUS database (calendar year 2002) to estimate annual 

mileage accrual and modeled odometer readings for MHDDT. We did not differentiate 

between in-state and interstate MHDDT with modeled odometer readings. Staff used 

CA IRP data to calculate the fraction of total mileage accrual that occurs in California on 

average. Staff estimated that CA IRP MHDDT drive 63% and out-of-state registered 
G-20 

MHDDT 8% of their total annual miles in California. We estimated the average 

odometer reading as 161,000 miles for out-of-state MHDDT and CA IRP MHDDT and 

207,000 miles for instate MHDDT. We estimated the average annual mileage accrual to 

be 22,000 miles for out-of-state and CA IRP MHDDT and 20,000 miles for instate 

MHDDT. Of the 22,000 miles driven annually by out-of-state and CA IRP MHDDTs, it 

was assumed that out-of-state vehicles drive only 8% (1,800 miles) of their annual miles 

in California as compared to 64% (14,000 miles) for CA IRP vehicles. The difference in 

average annual accrual resulted from the different age distribution representing the 

population within each category. In Figure 8 we plot the annual accrual in California 

and cumulative modeled odometer readings for MHDDT by age and category. 

Figure 8. MHDDT Accrual and Modeled Odometer (2008) 

0

5,000

10,000

15,000

20,000

25,000

30,000

-1 1 3 5 7 9 1113151719212325272931333537394143

Age

Accrual (miles/yr)

0

400,000

800,000

1,200,000

1,600,000

2,000,000

2,400,000

Odometer (miles)

MH CA Instate Accrual MH CA Out of state Accrual

MH CAIRP Accrual MH CA Instate Odometer

MH CA Out of State Odometer MH CAIRP Odometer
G-21 

Figure 8 shows the annual mileage accrual rates to be similar for medium-heavy duty 

diesel CA Instate, CA Out-of-State, and CA IRP-registered trucks. However, these 

accrual rates are simply the total accrual rates for the vehicles in each inventory 

category; they do not represent the accrual rate for vehicles in each category within 

California. Because the share of annual travel within California varies between 

categories (8% for Out-of-State; 63% for CA-IRP; 100% for MHDDT Instate), the 

accrual rate for vehicles within California also varies for each category. We show these 

accrual rates in Figure 9; because California-registered MHDDT are assumed to spend 

100% of their time in California, they have the highest CA accrual rate. 

Figure 9. MHDDT Accrual in California and Modeled Odometer (2008) 

MH CA Instate

CA Accrual

MH CA Out of State

CA Accrual

MH CAIRP

CA Accrual MH CA Instate, CAIRP,

and Out of State Odometer

0

5,000

10,000

15,000

20,000

25,000

30,000

-1 1 3 5 7 9 1113151719212325 272931333537394143

Age

Accrual (miles/yr)

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

Odometer (miles)

MH CA Instate CA Accrual MH CA Out of State CA Accrual

MH CAIRP CA Accrual MH CA Instate Odometer

MH CA Out of State Odometer MH CAIRP Odometer

c) Base Year Vehicle Miles Traveled by Fleet Size and Mileage 

Thresholds 

We categorized medium-heavy duty diesel trucks by fleet size as single-truck fleets, 

two-truck fleets, three-truck fleets, and fleets of more than three trucks. We also 

categorized MHDDT based on their annual miles driven since the regulation is to be 

applied differently to vehicles used at differing mileage levels. The mileage threshold of 

significance for MHDDT from a regulatory perspective is 5,000 miles per year. In Table 

8, Table 9, and Table 10 we show the distribution of daily VMT driven in California in 

calendar year 2008 by different size fleets of instate, CA-IRP, and out-of-state 

MHDDTs, respectively. Each table also shows the VMT-weighted average age of the 

trucks as a function of fleet size. For all categories of MHDDT, regardless of miles 

driven or state of registration, the smaller the fleet, the older the truck. Also, very little 
G-22 

(less than 2%) of the total statewide MHDDT VMT is driven by vehicles in fleets driving 

less than 5,000 miles a year. In addition, MHDDTs driving less than 5,000 miles a year 

are significantly older (4 to 6 years older) than those in comparable size fleets that drive 

more than 5,000 miles a year. 

Table 8. Distribution of VMT among Medium-Heavy Instate Vehicles by Fleet Size 

(2008) 

 Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 80,942 0.6% 13.4 

2 trucks 25,478 0.2% 13.1 

3 trucks 15,111 0.1% 13.0 

Below 5000 miles 

> 3 trucks 107,142 0.8% 11.6 

1 truck 3,786,410 29.7% 7.0 

2 trucks 1,244,223 9.8% 6.8 

3 trucks 740,914 5.8% 6.9 Above 5000 miles

> 3 trucks 6,731,027 52.9% 5.7 

 Total 12,731,247 100.0% 

Table 9. Distribution of VMT among Medium-Heavy CAIRP Vehicles by Fleet Size 

(2008) 

 Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 95 0.1% 8.2 

2 trucks 61 0.1% 8.4 

3 trucks 68 0.1% 9.2 

Below 5000 miles 

> 3 trucks 616 0.8% 9.4 

1 truck 9,684 13.0% 4.3 

2 trucks 6,018 8.0% 4.3 

3 trucks 5,943 7.9% 4.5 Above 5000 miles

> 3 trucks 52,292 69.9% 4.7 

 Total 74,777 100.0% 
G-23 

Table 10. Distribution of VMT among Medium-Heavy Out-of-state Vehicles by 

Fleet Size (2008) 

 Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 54 0.1% 8.2 

2 trucks 35 0.1% 8.4 

3 trucks 39 0.1% 9.2 

Below 5000 miles 

> 3 trucks 353 0.8% 9.4 

1 truck 5,552 13.0% 4.3 

2 trucks 3,450 8.0% 4.3 

3 trucks 3,407 7.9% 4.5 Above 5000 miles

> 3 trucks 29,980 69.9% 4.7 

 Total 42,871 100.0% 

5. California Registered Heavy Heavy Duty Diesel Trucks 

California is somewhat unique in the United States in that it has a comparatively large 

population of older heavy-heavy duty diesel trucks. These trucks, with an average age 

of 10 to 12 years, generally do not travel outside California, operating practically as a 

captive fleet. These California registered trucks drive fewer miles per truck than IRP 

trucks registered in California, both because they are older and less mechanically 

reliable and because they are engaged in more localized trucking services than their 

out-of-state registered counterparts. Consequently, California registered HHDDT are 

much more likely to drive in the air basin in which they are primarily based. 

When evaluating VIUS data we realized there is a significant difference in the mileage a 

truck travels depending upon whether it is a single-unit truck or a combination tractor. 

On average, combination tractors drive more miles per year than single-unit trucks. We 

also found that while single-unit trucks tend to be purchased new in California and then 

operate in California for the life of the truck, in-state tractors tend to begin their life as 

interstate trucks and then transition into in-state usage as they age and accrue miles. 

Analysis of the VIUS data indicates interstate trucks are generally sold into the in-state 

fleets between 2 and 6 years of age, having accrued approximately 500,000 miles in 

interstate service. 

a) Base Year Population and Age Distribution by Fleet Size 

To develop population estimates for in-state HHDDT, staff used DMV registration data 

(license plate number and GVWR) to identify those HHDDT that operate solely within 

California (in-state). Staff then analyzed DMV and IRP data to identify only California 

registered IRP trucks. Next, staff used the DMV data to assess the body type of each 

in-state truck. We then used the DMV data to estimate the population and model year 

distribution of in-state single-unit trucks, in-state tractors, and California IRP trucks. We 

subtracted the populations of utility trucks, drayage trucks, and agricultural trucks 

estimated elsewhere from the in-state HHDDT category, by body type and registration, 

to estimate the number of in-state tractors and avoid double counting. 
G-24 

In Figure 10 we provide the estimated population and age distribution for in-state 

HHDDT categories in California in calendar year 2008. In general, vehicles traveling 

only in California are older than those traveling out-of-state and single unit trucks are 

slightly older than comparable model year tractors. As shown in Figure 10, CA IRP 

trucks have a population-weighted average age of 6.2 years, while in-state tractors 

average age 9.9 years, and single-unit trucks average 10.2 years old. 

Figure 10. California-Registered In-State HHDDT Population and Model Year 

Distribution (2008) 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

HH Instate HH Single Unit HH CAIRP

b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds 

Staff used VIUS to assess annual mileage accrual rates by body type for in-state 

HHDDT and CA-IRP trucks. This analysis indicated that California IRP trucks drive 

about 55% of their total miles in California. Because used trucks are sold into the instate tractor category from interstate categories, and interstate category tractors drive 

more miles than in-state tractors during their early years, we developed a composite 

modeled odometer to represent in-state tractors. The composite odometer estimate 

assumes a high annual mileage in early model years when a tractor is in long-haul 

service and lower annual mileage once a tractor begins shorter haul service in the instate category. For comparative purposes, a vehicle of a given model year that had 

been used exclusively for interstate purposes would have a higher odometer reading 

than a vehicle of the same age that had been used exclusively for instate purposes; a 

vehicle that had been used earlier in its life for interstate purposes and later for instate 

purposes would likely have an odometer reading between the two. The average 

odometer reading estimated using this approach was 723,000 miles for instate HHDDT, 

338,000 miles for single-unit HHDDT, and 668,000 miles for CAIRP HHDDT. The 
G-25 

average annual mileage accrual was estimated to be 51,000 miles for instate HHDDT, 

25,000 miles for single-unit HHDDT, and 75,000 miles for CAIRP HHDDT (of which 

43,000 miles are driven in California). Figure 11 provides in-state HHDDT mileage 

accrual and modeled odometer by model year; these data reflect the assumption of 

composite use. For comparative purposes, in Figure 12 we provide the composite 

odometer reading for instate trucks of various model years in calendar year 2008 

compared with the modeled odometer reading for similar trucks used purely for in-state 

or interstate long-haul traffic. 

Figure 11. HHDDT In-state and Interstate Fleet Accrual and Modeled Odometer 

(2008) 

0

20,000

40,000

60,000

80,000

100,000

120,000

-1 1 3 5 7 9 1113151719212325272931333537394143

Age

Accrual (miles/yr)

0

400,000

800,000

1,200,000

1,600,000

2,000,000

2,400,000

Odometer (miles)

HH CA Instate Accrual HH Single Unit Accrual

HH CAIRP Accrual HH CA Instate Odometer

HH CA Single Unit Odometer HH CAIRP Odometer
G-26 

Figure 12. HHDDT In-State Tractor Modeled Odometer (2008) 

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Odometer (miles)

Pure Instate Tractor Adjusted Instate Tractor Pure Interstate Tractor
G-27 

c) Base Year Vehicle Miles Traveled by Fleet Size and Mileage 

Thresholds 

We categorized heavy-heavy duty diesel trucks by fleet size as single-truck fleets, twotruck fleets, three-truck fleets, and fleets of more than three trucks. We also 

categorized the fleets by their annual VMT since the regulation is to be applied 

differently to trucks driven at different mileages each year. The mileage threshold of 

significance for HHHDT from a regulatory perspective is 7,500 miles per year. In Table 

11 we show the distribution of daily VMT and the VMT-weighted average age as a 

function of fleet size and annual miles driven for heavy-heavy instate trucks in California 

in 2008. Heavy-heavy duty diesel instate trucks driving less than 7,500 miles a year are 

significantly older (5 years) than their counterparts driving more than 7,500 miles a year 

and contribute less than 2% of the total VMT driven each year by in-state HHDDTs. 

Regardless of miles driven, HHDDT in smaller fleets are on average older than those in 

larger fleets. 

Table 11. Distribution of Daily VMT among California Instate Tractors HeavyHeavy Duty Vehicles by Fleet Size (2008) 

 Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 44,925 0.4% 15.2 

2 trucks 12,992 0.1% 15.4 

3 trucks 7,370 0.1% 15.3 

Below 7500 miles 

> 3 trucks 48,792 0.5% 13.6 

1 truck 3,031,868 29.1% 10.1 

2 trucks 902,069 8.7% 9.7 

3 trucks 529,836 5.1% 9.5 Above 7500 miles

> 3 trucks 5,835,899 56.0% 6.4 

 Total 10,413,751 100.0% 
G-28 

In Table 12 we show the distribution of daily VMT as a function of fleet size and mileage 

driven for heavy-heavy single-unit trucks operating in California in calendar year 2008. 

In Table 13 we show the distribution of daily VMT as a function of fleet size and mileage 

driven in California for heavy-heavy trucks licensed in California under the International 

Registration Program in California in 2008. Each table also shows the average VMTweighted age of vehicles within the fleets of different sizes; in each case, the trucks 

driven fewer miles tended to be older than trucks driven more miles, and the average 

age of vehicles within a fleet was also estimated to decrease with the size of the fleet. 

Table 12. Distribution of Daily VMT among Single-Unit Heavy-Heavy Duty 

Vehicles by Fleet Size (2008) 

 Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 37,585 1.1% 15.6 

2 trucks 13,153 0.4% 15.5 

3 trucks 7,951 0.2% 14.8 

Below 7500 miles 

> 3 trucks 63,491 1.9% 13.5 

1 truck 766,081 22.5% 10.3 

2 trucks 279,769 8.2% 9.9 

3 trucks 185,491 5.4% 9.2 Above 7500 miles

> 3 trucks 2,057,340 60.3% 7.1 

 Total 3,410,860 100.0% 

Table 13. Distribution of Daily VMT among California International Registration 

Program Vehicles in California by Fleet Size (2008)

 Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 5,804 0.1% 10.8 

2 trucks 2,096 0.0% 10.9 

3 trucks 1,223 0.0% 11.4 

Below 7500 miles 

> 3 trucks 9,801 0.1% 11.3 

1 truck 2,198,998 26.5% 6.0 

2 trucks 812,823 9.8% 5.8 

3 trucks 460,037 5.6% 5.7 Above 7500 miles

> 3 trucks 4,791,942 57.9% 4.6 

 Total 8,282,725 100.0% 

6. Out-of-State Heavy Heavy Duty Diesel Trucks 

According to the California Department of Finance, California’s economy is the eighth 

largest in the world (DOF, 2008). Foreign trade, construction, and transportation are all 

major contributors to California’s economy, and these economic sectors attract the 

services of national trucking fleets including large numbers of out-of-state heavy-heavy 

duty diesel trucks. 
G-29 

a) Base Year Population and Age Distribution by Fleet Size 

No single source of information describes out-of-state truck activity in California. To 

develop an inventory, staff conducted detailed research into the IRP and IFTA 

programs. The California DMV provided staff with a report providing the total number of 

out-of-state trucks, by state, that are enrolled in the IRP program and in fleets that 

reported any travel in California. These data suggested that more than one million 

trucks are in fleets that report mileage in California. However, this is likely to be an 

upper estimate as not all of the trucks in these fleets actually enter California. To better 

constrain the number of out-of-state trucks entering California, staff analyzed IRP data 

in greater detail, as described below. 

In analyzing the age distribution of out-of-state trucks, staff divided heavy-heavy duty 

trucks into two groups based on their proximity to California under the assumption that 

trucks traveling longer distances are younger. Neighboring states were considered to 

include British Columbia, Washington, Oregon, Idaho, Nevada, and Arizona while nonneighboring states included all other states and Canadian provinces. Staff sampled IRP 

data from 12 states to obtain a statistically representative sample of data. IRP data 

suggested neighboring state trucks are on average 6.1 years old and have a model year 

distribution similar to that of California interstate trucks while non-neighboring state 

trucks are on average around 3.3 years old, much younger than other interstate trucks. 

Recent field studies supported by the ARB have confirmed this trend (Lutsey, 2008). 

To better understand the population of out-of-state trucks that actually operate in 

California, staff developed a methodology to assess the number of trucks in a fleet likely 

to enter California. We evaluated all of the collected non-California registered IRP data 

by fleet. For each fleet we calculated the number of miles traveled in California per 

truck as reported in the IRP data. We then assumed an average trip length depending 

on the registered location of each fleet and where those trucks would most likely travel 

in California. In many cases the assumed trip length was longer than the calculated 

number of miles traveled per truck in the IRP data. In those cases, we calculated the 

population operating in California as the total miles traveled in California divided by the 

assumed trip length. For all other fleets we assumed all of the reported trucks in that 

fleet actually enter California. Staff then recompiled the population for trucks from 

neighboring states and non-neighboring states. The results suggested for fleets in 

states neighboring California, approximately 60% of trucks in those fleets authorized to 

enter California actually do so. Of fleets in states not neighboring California, 

approximately 40% of the trucks authorized to enter California are estimated to actually 

do so. 
G-30 

The fraction of owner-operators in the out-of-state category was based on the IRP 

samples collected. The population-weighted average age of HHDDT from neighboring 

states was estimated to be 6.2 years while HHDDT from non-neighboring states were 

estimated to be on average 3.6 years old. These estimates were each close to the IRP 

estimates. In Figure 13 we provide estimates of the out-of-state population and model 

year distribution of heavy-heavy duty diesel trucks operating in California in calendar 

year 2008. 

Figure 13. Out-of-State HHDDT Population and Model Year Distribution (2008) 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

HH NOOS HH NNOOS
G-31 

b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds 

Staff used VIUS to assess accrual rates for the nationwide IRP truck category traveling 

in California. Staff then evaluated IRP sample data to estimate the fraction of miles 

accrued in California and 2005 IFTA records to quantify the total miles traveled in 

California by out-of-state HHDDT. Results suggested that trucks from neighboring 

states travel on average 40% of their total mileage in California, while trucks from nonneighboring states travel around 10% of their total mileage in California. The average 

odometer reading was estimated to be 668,000 miles for neighboring out-of-state 

HHDDT and 473,000 miles for non-neighboring out-of-state HHDDT. The average 

annual mileage accrual was estimated as 75,000 miles per year (30,000 miles per year 

in CA) for neighboring out-of-state HHDDT and 85,000 miles per year (8,400 miles per 

year in CA) for non-neighboring out-of-state HHDDT. In Figure 14 we provide estimates 

of the average annual mileage accrual and modeled odometer by model year for out-ofstate heavy-heavy duty diesel trucks operating in California in calendar year 2008. 

Figure 14. Out-of-State Truck Accrual and Modeled Odometer (2008) 

0

20,000

40,000

60,000

80,000

100,000

120,000

-1 1 3 5 7 9 1113151719212325272931333537394143

Age

Accrual (miles/yr)

0

400,000

800,000

1,200,000

1,600,000

2,000,000

2,400,000

Odometer (miles)

HH NOOS Total Accrual HH NNOOS Total Accrual

HH NOOS Odometer HH NNOOS Odometer
G-32 

In Figure 14, the accrual rates for HHDDT NOOS and NNOOS trucks are shown to be 

equal, but these rates represent the overall accrual for the trucks. Because trucks from 

states neighboring California drive a larger fraction of their annual miles in California 

than trucks from non-neighboring states, the model used for this analysis reflects 

different accrual rates for vehicles in each category in California. We show these in 

Figure 15. 

Figure 15. Out-of-State Truck Accrual within California and Modeled Odometer 

(2008) 

HH NOOS & NNOOS

Total Accrual

HH NOOS CA Accrual

HH NNOOS

 CA Accrual

HH CA NOOS & NNOOS 

Odometer

0

20,000

40,000

60,000

80,000

100,000

120,000

-1 1 3 5 7 9 1113151719212325272931333537394143

Age

Accrual (miles/yr)

0

400,000

800,000

1,200,000

1,600,000

2,000,000

2,400,000

Odometer (miles)

HH NOOS Total Accrual HH NNOOS Total Accrual

HH NOOS CA Accrual HH NNOOS CA Accrual

HH NOOS Odometer HH NNOOS Odometer

c) Base Year Vehicle Miles Traveled by Fleet Size and Mileage 

Thresholds 

Staff also categorized heavy-heavy duty diesel trucks from outside California by fleet 

size as single truck fleets, two truck fleets, three truck fleets, and fleets of more than 

three trucks. We also categorized fleets based on the annual VMT of trucks, since the 

regulation is to be applied differently to fleets used at differing mileage levels. The 

mileage threshold of significance for HHDDT from a regulatory perspective is 7,500 

miles per year. In Table 14 we show the distribution of daily VMT driven in California in 

calendar year 2008 by heavy-heavy diesel trucks of different fleet sizes registered in 

states neighboring California. In Table 15 we show the distribution of daily VMT driven 

in California in calendar year 2008 by heavy-heavy diesel trucks registered in nonneighboring states in 2008. Each table also shows the average VMT-weighted age of 

vehicles within each fleet size; we estimated the average age to be higher for vehicles 
G-33 

driven fewer miles. We estimated the average age to be independent of the fleet size 

for vehicles registered outside California. 

Table 14. Distribution of Daily California VMT among Heavy-Heavy Duty Diesel 

Trucks Registered in Neighboring States by Fleet Size (2008) 

 Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 1,149 0.0% 11.1 

2 trucks 357 0.0% 11.1 

3 trucks 261 0.0% 11.1 

Below 7500 miles 

> 3 trucks 7,253 0.2% 11.1 

1 truck 501,599 12.7% 5.1 

2 trucks 155,808 3.9% 5.1 

3 trucks 113,815 2.9% 5.1 Above 7500 miles

> 3 trucks 3,167,429 80.2% 5.1 

 Total 3,947,672 100.0% 

Table 15. Distribution of Daily VMT among Heavy-Heavy Duty Diesel Trucks 

Registered in States not Neighboring California by Fleet Size (2008) 

 Fleet Size Daily VMT Share VMT-weighted

Average Age 

1 truck 188 0.0% 8.3 

2 trucks 58 0.0% 8.3 

3 trucks 41 0.0% 8.3 

Below 7500 miles 

> 3 trucks 11,610 0.1% 8.3 

1 truck 192,875 1.6% 3.1 

2 trucks 58,926 0.5% 3.1 

3 trucks 41,740 0.3% 3.1 Above 7500 miles

> 3 trucks 11,889,217 97.5% 3.1 

 Total 12,194,654 100.0% 

7. Buses 

The proposed regulation achieves emissions reductions for two classes of buses: 

school buses and other buses. School buses may be either privately- or publiclyowned, but must be used exclusively for transporting students in accordance with the 

definition of school buses in the California Vehicle Code. Other buses are defined as all 

buses identified in the DMV database that cannot be categorized as school buses and 

are not owned by public transit agencies. This category includes intercity buses, charter 

buses, and church buses. This section describes the assumptions used to develop a 

baseline emissions inventory for school buses and other buses. 

a) Base Year Population and Age Distribution by Fleet Size 

Staff used a calendar year 2005 database of school buses compiled by the CHP to 

estimate the population and age distribution of school buses in California in 2005 

(California Highway Patrol, 2006). The age distribution of school buses in 2005 was 
G-34 

assumed to be consistent with the age distribution of school buses between 2000 and 

2004. 

Staff used the survival rate of school buses in EMFAC2007 and the existing Lower 

Emissions School Bus Program (LESBP) to estimate the age distribution of school 

buses in future years. Specifically, the impact of the LESBP was modeled as though: i) 

all pre-1985 model year school buses will be replaced by buses that meet 2007 

emission standards as of December 31, 2009, and ii) 50% of 1985 and 1986 model year 

school buses will be replaced with school buses that meet 2007 emission standards by 

December 31, 2010. In addition, the maximum age for a school bus was set at thirty 

years; any school bus older than thirty years was assumed to be replaced with a new 

bus. Any school bus that was retired due to age or attrition was assumed to be replaced 

in the population with a new school bus. The aggregate population of diesel school 

buses was assumed to remain unchanged; any increases to the total school bus 

population were assumed to represent new vehicles using natural gas or some other 

alternative fuel entering the fleet. 

Other buses were assumed to follow the age distribution of such vehicles as modeled in 

EMFAC2007. The population-weighted average age was estimated to be 12.2 years for 

school buses and 9.6 years for other buses. We compare the age distribution of school 

buses and other buses operating in California as modeled for calendar year 2008 in 

Figure 16. 

Figure 16. Bus Population and Model Year Distribution (2008) 

0

200

400

600

800

1,000

1,200

1,400

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

School Bus Other Buses
G-35 

b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds 

In the absence of new information, staff used the average annual mileage accrual and 

odometer estimates assumed for buses in EMFAC2007 to estimate the benefits of the 

proposed rule. The average odometer reading was estimated to be 173,000 miles for 

school buses and 222,000 miles for other buses. The average annual mileage accrual 

was estimated to be approximately 13,000 miles for school buses and 22,000 miles for 

other buses. All such miles are assumed accrue in California. We show in Figure 17 

the average annual mileage accrual and odometer estimates for school buses and other 

buses in California in calendar year 2008.. 

Figure 17. Bus Accrual and Modeled Odometer (2008)

0

5,000

10,000

15,000

20,000

25,000

30,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Accrual (miles/yr)

0

100,000

200,000

300,000

400,000

500,000

600,000

Odometer (miles)

School Bus Accrual Other Buses Accrual

School Bus Odometer Other Buses Odometer

c) Base Year Vehicle Miles Traveled by Fleet Size and Mileage 

Thresholds 

Buses were not differentiated by fleet size or mileage threshold in this inventory as 

buses are not treated differently in the proposed regulation based on these criteria. 

8. Power Take Off Operations in California 

Power Take Off (PTO) operations are those that result in emissions related to activities 

other than travel, such as a crane lifting objects or a cement mixer processing raw 

materials. Emissions associated with these operations are more accurately quantified 

using fuel consumption than vehicle miles traveled, the usual activity metric for on-road 

vehicles. 
G-36 

a) Estimating Fuel Usage 

Staff did not estimate the actual population or distribution of PTO activity. Instead we 

used fuel consumption data for PTO operations in California in calendar year 2005 as 

reported by the California Board of Equalization. 

b) Converting Fuel Usage to Activity by Age 

The age distribution PTO equipment was assumed to follow the same age distribution 

as HHDDT single-unit trucks. This distribution was given earlier in Figure 10. 

Mileage accrual was not directly estimated for PTO operations since, as mentioned 

previously, emissions generally occur while the equipment is stationary. Staff used the 

fuel consumption rate in EMFAC2007 for HHDDT single-unit trucks at twenty miles-perhour with the fuel consumption estimated by the CA Board of Equalization to backcalculate the VMT-equivalent for PTO activity in California, assuming the speed profile 

embedded in EMFAC2007. 

E. Emission Rates 

For most categories, staff have used EMFAC2007 and modeled cumulative mileage to 

develop category-specific emission rates. We have also updated several estimates of 

emission rates based upon recent data analysis. In this section we describe the 

methods used to estimate emission rates. In each case, a reference is provided for 

more detailed information as to the basis for the emission rates. The specific emission 

rates assumed for vehicles of each fleet will be provided in a database scheduled to be 

released in conjunction with the staff report.. 

1. Updated MHDDT Emissions 

In EMFAC2007, staff did not update the emission factors for medium heavy heavy-duty 

trucks (MHDDT). Subsequent to the release of EMFAC2007, the CRC made emissions 

test data available for MHDDT available to staff through its E55/59 project final report. 

This proposed regulation incorporates updated MHDDT emission factors developed by 

staff using the latest E55/59 study results. 

To revise the MHDDT emission factors for this analysis, staff merged the E55/59 data 

into the data set that was used to develop MHDDT emission factors for EMFAC2002. 

From this combined data set, staff then recalculated revised zero-mile rates (ZMR) and 

deterioration rates (DR) for MHDDT. 

The method for deriving MHDDT ZMR and DR is the same as that used for calculating 

emission factors for heavy heavy-duty trucks (HHDDT); the methodology has been 

described in detail in an EMFAC2007 technical memo (ARB, 2006f). 

In Table 16 we provide the zero-mile emission and deterioration rates for medium-heavy 

duty diesel trucks assumed in EMFAC2007. The deterioration rates are used to model 

the increase in emissions relative to the zero-mile (or new-vehicle) emission rate due to 
G-37 

a vehicle aging and accruing miles. In Table 17 we give the updated emission rates 

reflecting incorporation of the latest E55/59 data, as discussed previously. 

Table 16. EMFAC2007 MHDDT Zero-Mile Emission Rate (ZMR, g/mi) and 

Deterioration Rate (DR, g/mi/10,000 mi) 

MY HC CO NOx PM10

GROUP ZMR DR ZMR DR ZMR DR ZMR DR 

Pre 1975 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.016 

1975-76 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.016 

1977-79 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.016 

1980-83 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.016 

1984-86 0.33 0.014 2.99 0.131 17.91 0.043 1.00 0.021 

1987-90 0.21 0.016 1.80 0.140 15.74 0.034 0.73 0.017 

1991-93 0.18 0.018 1.43 0.139 13.11 0.078 0.45 0.022 

1994-97 0.11 0.017 0.78 0.121 11.55 0.048 0.27 0.018 

1998 0.09 0.014 0.64 0.097 10.52 0.032 0.24 0.012 

1999-02 0.09 0.014 0.64 0.097 10.52 0.032 0.24 0.012 

2003 0.09 0.007 1.04 0.074 5.79 0.018 0.29 0.009 

2004-06 0.09 0.006 1.04 0.074 5.48 0.017 0.29 0.009

2007-09 0.058 0.006 0.666 0.074 3.01 0.017 0.029 0.009 

2010+ 0.025 0.006 0.291 0.074 0.548 0.017 0.029 0.009 

Table 17. Revised MHDDT Zero-Mile Emission Rate (ZMR, g/mi) and Deterioration 

Rate (DR, g/mi/10,000 mi) 

HC CO NOx MY PM10

GROUP ZMR DR ZMR DR ZMR DR ZMR DR 

Pre 1986 0.83 0.047 2.79 0.159 15.61 0.033 0.97 0.038 

1987-90 0.65 0.056 2.19 0.189 15.39 0.044 1.05 0.034 

1991-93 0.29 0.025 1.12 0.095 11.51 0.053 0.57 0.026 

1994-97 0.21 0.028 0.83 0.109 11.30 0.068 0.31 0.017 

1998-02 0.22 0.028 0.84 0.108 11.11 0.078 0.35 0.015 

2003-06 0.14 0.013 0.37 0.033 7.35 0.077 0.22 0.008

2007 0.12 0.008 0.31 0.020 4.78 0.065 0.022 0.001 

2008 0.12 0.008 0.31 0.020 4.39 0.064 0.022 0.001 

2009 0.12 0.008 0.31 0.020 3.78 0.062 0.022 0.001 

2010 0.10 0.002 0.26 0.005 1.01 0.054 0.022 0.001 

2011 0.10 0.002 0.26 0.005 0.86 0.054 0.022 0.001 

2012 0.10 0.002 0.26 0.005 0.67 0.053 0.022 0.001 

2013+ 0.10 0.002 0.26 0.005 0.67 0.041 0.022 0.001 
G-38 

2. Revised engine market information 

Staff also updated the emission factors for heavy heavy-duty diesel trucks (HHDDT) 

and medium heavy-duty diesel trucks (MHDDT) to reflect manufacturers’ compliance 

with the 2007 engine standard and the anticipated compliance with the 2010 engine 

standard between 2006 and 2011. Staff estimated that the adjustment to 2006-2011 

model year emission factors would result in lower NOx emission rates for 2006 and 

2009 model years but higher NOx emission rates for 2007, 2008, 2010, and 2011 model 

years than the rates currently assumed in EMFAC2007. 

The latest information available to staff indicates that at least one engine manufacturer 

does not plan to use selective catalyst reduction technology in their 2010-2011 model 

year heavy-duty diesel engines. In addition, review of the certification data shows that 

some engine manufacturers introduced 1.2 g/bhp-hr NOx heavy duty engines one year 

earlier than required. As a result, staff decided to update the emission factors in 

EMFAC2007 to reflect the latest information from manufacturers and certification data. 

For the earlier introduction of engines meeting 1.2 g/bhp-hr NOx and 0.01 g/bhp-hr PM 

standards, the current zero-mile rates (ZMR) and deterioration rates (DR) for 2003-06 

and 2007-09 model year groups were weighted by the sales fractions of 2007 standard 

compliant engines in 2006 model year. 

For the introduction of 0.5 g/bhp-hr NOx engines in 2009 model year, staff estimated the 

ZMR by adjusting the current ZMR for 2006-2009 model year group (1.2 g/bhp-hr NOx

engines) but assumed that the deterioration rate for 2006-09 model year would still 

apply to the 0.5 g/bhp-hr NOx engines. The assumption of unchanged DR is largely 

based on the fact that the engine manufacturers will achieve a 0.5 g/bhp-hr NOx level 

with an integrated technology solution based on their 2006-09 model year technologies 

(advanced fuel system, air management, combustion and electronic controls, and 

enhanced cooled exhaust gas recirculation). 
G-39 

Staff estimated the 2005-2008 model year sales fractions of heavy and medium heavyduty diesel engines based on the sales fraction data reported by the manufacturers and 

projected the 2009-2012 model year sales fractions from the information currently 

available. We show the ARB-estimated penetration rates for 2005 to 2012 model years 

in Table 18. 

Table 18. ARB Estimated Penetration Rates of 2005-2012 Model Year Engines 

Certified NOx (g/bhp-hr) Certified PM10 (g/bhp-hr) 

Model Year 

2.2 1.2 0.5 0.25/0.2* 0.1 0.01 

Heavy Heavy-Duty Diesel 

2005 100% 

2006 99% 1% 98% 2% 

2007 14% 86% 1% 99% 

2008 7% 93% 100% 

2009 90% 10% 100% 

2010 10% 90% 100% 

2011 10% 95% 100% 

2012 100% 100% 

Medium Heavy-Duty Diesel 

2005 100% 

2006 100% 98% 2% 

2007 23% 77% 100% 

2008 12% 88% 100% 

2009 90% 10% 100% 

2010 10% 90% 100% 

2011 10% 90% 100% 

2012 100% 100% 

* 0.25 g/bhp-hour applies to 2010 model year only.

Using the sales fractions in Table 18, staff calculated the NOx ZMRs and DRs of both 

HHDDT and MHDDT for 2006-2011 model years by weighting the ZMRs and DRs of the 

corresponding model year groups. Staff did not revise the PM emission factors for 

HHDDT and MHDDT. As can be seen in Table 18, the sales of PM filter-equipped 

engines in 2006 account for only about 2% for both HHDDT and MHDDT categories; 

thus the impact of DPF engines on the PM emission rates for the 2006 model year is 

negligible. 
G-40 

In Table 19 we show the current and revised NOx ZMR and DR of both HHDDT and 

MHDDT for 2006 to 2011 model years. 

Table 19. NOx Emission Factors for 2006-2011 MY Heavy-Duty Diesel Trucks 

(ZMR in g/mi, DR in g/mi/10,000 mi) 

HHDDT MHDDT 

Current Revised Current Revised Model 

Year 

ZMR DR ZMR DR ZMR DR ZMR DR 

2006 12.54 0.0522 12.48 0.0521 7.35 0.0765 7.35 0.0765 

2007 7.66 0.0573 4.78 0.0654 

2008 7.25 0.0468 4.39 0.0638 

2009 

6.84 0.0465 

6.44 0.0464 

4.01 0.0621 

3.78 0.0621 

2010 1.72 0.0413 1.01 0.0540 

2011 

1.14 0.0407 

1.46 0.0413 

0.669 0.0531 

0.859 0.0540 

3. Carbon Dioxide (CO2) Emission Rates 

Reducing carbon dioxide (CO2) emissions is a primary goal of the State of California. 

CO2 emissions are generated through the combustion of fuels and in particular by 

combustion of fuels in trucks and buses that operate in California. CO2 emissions are a 

function of engine size, load, speed, miles traveled, and many other factors. This 

section describes how the staff derived CO2 emission rates to develop emissions 

inventories in support of the proposed regulation. 

Although EMFAC2007 provides CO2 emission rates for heavy duty diesel trucks, it 

assumes them to be constant regardless of model year, technology group, activity and 

other factors. To develop more finely resolved CO2 emission rates for trucks, staff used 

fuel economy data as a surrogate for CO2 since there is a larger database of fuel 

economy data available for trucks than CO2 data. Fuel economy estimates were 

converted to CO2 emission rates based on the carbon content of diesel fuel, as 

discussed in more detail later in this section. 

In this analysis we have evaluated available data to determine how improvements in 

engine technology and increasingly stringent criteria pollutant emission control 

requirements have affected the fuel economy of HHDDT and, consequently, CO2

emission rates. Staff has recently reviewed the following data sources to develop 

model year-specific fuel economy values for HHDDTs operating in California: 

• Department of Energy: Calendar Year Fleet Average from 1970-2006 (United 

States Department of Transportation, 2007) 

• CRC E55/59 study by West Virginia University: Model Year 1988-2003 

• International Fuel Tax Agreement (IFTA) for trucks operating in California 

• Consent Decree, in-use study by West Virginia University: Model Year 1994-

2003 
G-41 

We chose to not use the Department of Energy (DOE) fuel economy data directly since 

those are national data and therefore not representative of the truck fleet or driving 

conditions found in California. Also, the DOE data do not provide information on how 

fuel economy and CO2 emission rates vary as a result of technology. DOE data were 

used instead as an independent check on our technical analysis. 

 

Our technical analysis shows differences in fuel economy between different technology 

groups (represented as model year groups), as shown in Table 20. This table provides 

the estimated HHDDT fuel economy by model year group, as well as the assumptions 

that are embedded in those estimates. 

Table 20. Proposed Fuel Economy Values for HHDDT Operating in California 

MY MPG Note 

Pre-1988 5.20 100% Mechanically controlled engines (100% @ 5.2 mpg) 

1988-1990 5.39 25% Phase-in of electronic control (75% @ 5.2 mpg and 25% @ 5.95 mpg) 

1991-1993 5.58 50% Phase-in of electronic control (50% @ 5.2 mpg and 50% @ 5.95 mpg) 

1994-1995 5.76 75% Phase-in of electronic control (25% @ 5.2 mpg and 75% @ 5.95 mpg) 

1996 5.95 100% Phase-in of electronic control (100% @ 5.95 mpg) 

1997-1998 5.95 Same Fuel Economy as MY 1996 engines

1999-2002 5.48 Post consent Decree Engines with 8% loss is Fuel Economy (Timing Retarding) 

2003-2006 5.75 5% gain in Fuel Economy (Better Combustion Strategies) 

2007 5.61 3% loss in Fuel Economy due to EGR + DPF (86% @5.58 mpg; 14% @5.75 mpg) 

2008 5.59 3% loss in Fuel Economy due to EGR + DPF (93% @ 5.58 mpg; 7% @ 5.75 mpg) 

2009 5.58 3% loss in Fuel Economy due to EGR + DPF (100% @ 5.58 mpg) 

2010 5.78 4% gain in Fuel Economy due to SCR (90% @ 5.80 mpg and 10% @ 5.58 mpg) 

2011 5.78 4% gain in Fuel Economy due to SCR (90% @ 5.80 mpg and 10% @ 5.58 mpg) 

2012 5.80 4% gain in Fuel Economy due to SCR (100% @ 5.80 mpg) 

The assumptions described in Table 20 are estimated based on the following 

assumptions that were derived from analysis of available data: 

• Mechanical vs. Electronically Controlled Engines (ECE) 

o 5.95 mpg fuel economy for electronic engines from CRC E55/59 data 

o 0.75 mpg difference in fuel economy between mechanical control and 

electronic control from CRC E55/59 data 

o Assume all model year mechanical control engines have same fuel 

economy (5.20 mpg) 

o Phase-in of Electronic Controlled Engines 

 MY 1988 to 1990: 25% 

 MY 1991 to 1993: 50% 

 MY 1994 to 1995: 75% 

 MY 1996: 100% 
G-42 

• Off-Cycle operation in Electronic Controlled Engines 

o MY 1993-1998 

o Higher fuel economy and higher NOx than mechanical control engines. 

o Model year fuel economy of 5.95 mpg does not reflect chip reflash 

o Adjusted for chip reflash beginning calendar year 2002 

• MY 1999-2002: 

o 8% fuel penalty 

o Post-Consent Decree engines with injection timing retarding for NOx

control 

• MY 2003-2006: 

o 5% gain in fuel economy 

o Introduction of advanced combustion technologies including exhaust gas 

recirculation (EGR) 

• MY 2007-2009: 

o Introduction of diesel particulate filter (DPF) used for PM control 

o 3% fuel penalty for meeting 2007 emissions standards 

o 86% of engines would meet 2007 emissions standards in CY 2007 (ARB, 

2008a) 

o 93% of engines would meet 2007 emissions standards in CY 2008 

o 100% of engines would meet 2007 emissions standards in CY 2009 

• MY 2010-2012: 

o Introduction of selective catalytic reduction (SCR) for NOx control 

o 4% gain in fuel economy for engines equipped with (SCR) 

o 90% of engines would meet 0.2 g/bhp-hr NOx standards in 2010 and 2011 

o 10% of engines would meet 0.5 g/bhp-hr NOx standards with fuel 

economy of 5.58 mpg in 2010 and 2011 

o 100% of engines would meet 0.2 g/bhp-hr NOx standards in 2012 (ARB, 

2008c) 
G-43 

We applied the fuel economy estimates for each model year across each inventory 

category to develop a composite fuel economy by calendar year. Figure 18 compares 

fuel economy estimates by calendar year against values derived from DOE, IFTA, and 

EMFAC2007. This figure shows that HHDDT fuel economy is estimated to improve 

over time due initially to the introduction of electronically controlled engines and later 

due to the introduction of SCR-equipped engines. These results compare remarkably 

well with EMFAC, DOE, and IFTA data. For example, the composite fuel economy 

reported by all trucks operating in California in the IFTA program is 5.7 miles per gallon, 

which is equivalent to our estimates for interstate trucks. DOE data appear more 

variable across calendar years, probably due to varying sample size and 

representativeness in each year of reported data. Even so, DOE data are within 10% of 

ARB estimates, and EMFAC2007 is lower than current estimates by only 4 to 7%. In 

Figure 18 we show HHDDT fuel economy for interstate trucks only. In-state trucks, 

which are older on average than their interstate counterparts, are estimated to have 

slightly lower fuel economy (3% to 5%) than interstate trucks. 

Figure 18. Fleet Average Fuel Economy for Trucks Operating in California 

5.0

5.2

5.4

5.6

5.8

6.0

6.2

6.4

2000 2005 2010 2015 2020 2025

Calendar Year

Fleet Average Fuel Economy (mpg) 

EMFAC 2007

(~5.4 mpg)

California

IFTA Data

DOE Data

Heavy-duty 

interstate trucks 
G-44 

To convert fuel economy data to CO2 emission rates, staff used the following 

methodology: 

• estimate the fuel consumption, in gallons, for each truck category based upon 

the model year fuel economies and model year distribution 

• multiply the estimated fuel consumption by the carbon content of diesel fuel 

(2,778 g carbon/gallon diesel (USEPA (2008)) to estimate the carbon emitted by 

each truck category 

• multiply the estimate of carbon emitted by each category by the ratio between 

the molecular weight of CO2 and the molecular weight of carbon (44/12) to 

estimate the CO2 emissions from each category if 100% efficient 

• assume 99% efficiency, multiplying the CO2 emitted at 100% efficiency by 0.99, 

to estimate the final CO2 emissions estimate for each category. 

The application of this process is equivalent to applying an estimate of 22.2 lbs 

CO2/gallon diesel fuel (10.08 kg CO2/gallon diesel fuel) to the estimate of fuel consumed 

by vehicles in each category. 

Fuel economy in MHDDT was assumed to be 33% higher than for HHDDT in the same 

model year, based on analysis of data described above. CO2 emission rates for buses 

were taken directly from EMFAC2007. 

4. Final emissions rates 

Staff estimated the emission rates for each truck inventory category for two types of 

activity – vehicles in motion and vehicles idling. We estimated emission rates for 

vehicles in motion in terms of tons/day for the average vehicle; these are shown in the 

database provided in conjunction with this appendix. We estimated the emission rates 

for vehicles while idling in terms of tons/hour; these, too, are shown in the database 

provided with this appendix. 

F. Forecasting the Baseline Inventory 

In this section we describe the methods used to estimate the growth of future truck 

populations and truck VMT in the emissions inventory. 

1. Growth 

In modeling the emissions for future years, staff needed to estimate the amount of 

vehicular travel in future years. The emissions are in general proportional to the vehicle 

miles traveled. The growth rate for VMT is likely to vary between inventory categories; 

thus we estimated the VMT growth on a category-specific basis. 

For utility trucks in this inventory, staff assumed the same growth rate (2.0%) as was 

assumed in the regulation adopted by the ARB for public and utility fleets (ARB, 2005). 
G-45 

Staff projected the population of drayage trucks to increase at 2.5% per year near the 

Ports of Los Angeles, Long Beach, and Oakland. We estimated the VMT associated 

with the drayage trucks at each of these ports/railyards to grow at a slightly faster rate. 

We estimated the population of drayage trucks at other California ports/railyards to 

increase at a rate between 1.5% and 1.6% per year, with the VMT also increasing at a 

slightly faster rate. We estimated future VMT using factors such as projected container 

traffic and projected vessel traffic that resulted from past data and survey data. 

For agricultural trucks, staff assumed a negative growth rate of 0.31% statewide to 

reflect anticipated ongoing loss of farmland. This growth rate, which actually varies 

regionally, was developed in 2005 based on analysis of historical farmland acreage 

trends and with the assistance of agricultural stakeholders. The average annual 

mileage accrual of each agricultural truck was assumed to remain constant; thus the 

total annual VMT for agricultural trucks is assumed to decrease proportional to the truck 

population. 

For all other truck types, we projected the VMT to grow at a rate equivalent to the 

overall VMT growth rate for trucks reported in EMFAC2007 which is 2.66% for heavyheavy duty trucks and 1.62% for medium-heavy duty trucks. The annual VMT accrual of 

vehicles in these categories was assumed to remain constant; thus the population of the 

vehicles, in each category, was assumed to grow in proportion to the VMT. In Table 21 

we summarize the annual growth rate estimated for VMT within each truck category. 

Table 21. Annual VMT Growth Rates Projected by Category, 2008-2023 

Inventory Category VMT Growth Rate

MH Utility 2.00% 

HH Utility 2.00% 

HH Drayage near Oakland 5.38% 

HH Drayage near LA/LB 4.93% 

HH Drayage elsewhere 1.58% 

MH Agriculture -0.31% 

HH Agriculture -0.31% 

MH Instate 1.62% 

MH CAIRP 1.62% 

MH Out of State 1.62% 

HH Instate 2.66% 

HH Single-unit 2.66% 

HH CAIRP 2.66% 

HH Non-neighboring Out-of-State 2.66% 

HH Neighboring Out-of-State 2.66% 

School Bus -0.50% 

Other Bus 3.65% 

Power Take Off 2.64% 

VMT-weighted Average 2.48% 
G-46 

Staff revisited VMT growth to examine the extent to which more current economic data 

could result in a revised VMT growth rate, different from that currently assumed in 

EMFAC2007. Staff regressed national VMT data from the Bureau of Transportation 

Statistics (BTS) against the US Gross Domestic Product (GDP) for the trucking industry 

nationwide, which was also released by the BTS. This relationship was then projected 

with a prediction of the future trucking GDP to estimate the future VMT, again on a 

federal level. The future nationwide trucking GDP was predicted by extending the 

relationship regressed earlier between the nationwide trucking GDP and the 

employment in the transportation sector predicted in the State of California Economic 

Forecast for the Sacramento Forecast Project. We show the results of this model in 

Figure 19. 

Figure 19. The Historic and Projected Relationship between VMT and GDP on a 

Nationwide Level 

0

50,000

100,000

150,000

200,000

250,000

1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

VMT (Millions)

Observed truck miles, combination Nationwide projected truck miles

VMT ~ 48,000 + 900*Trucking GDP

where GDP is in billions

 VMT is in millions

The scale factor between nationwide VMT projected for future years and the nationwide 

VMT in 2007 was used with the VMT in California in 2007 to project future growth in 

California VMT. The resulting projections did not differ significantly from those that were 

estimated in the EMFAC2007 model; thus, staff decided to maintain the growth rates in 

EMFAC2007 as those for the overall California heavy duty truck fleet. 

 

2. Attrition 

For each vehicle category, staff assumed that, outside of regulatory impacts, the age 

distribution of vehicles within each inventory category would remain constant. Thus, the 

fraction of a vehicle class represented by vehicles of a certain age would remain 
G-47 

constant; for example, the fraction of MY 2000 vehicles in the category in calendar year 

2008 was assumed to be equivalent to the fraction of MY 2001 vehicles in the category 

in calendar year 2009. 

3. Pre-buy 

When regulations are approved, past experience has indicated that the impact of these 

regulations can shift the purchase pattern of truck operators. A technology that is 

required in one year but not in another year may cause trucks from one year to be more 

expensive than, and thus preferred to, trucks of another year. A trucking firm might 

wish to not buy a truck with unproven technology; thus, they may delay their purchase 

for several years until the technology is more proven or purchase trucks with known 

technology in the year(s) preceding the regulation. Although our analysis has indicated 

that the shift in purchasing behavior related to “pre-buy” is less than that from general 

economic trends, we have attempted to incorporate this behavioral pattern in our 

assessment of model year distribution for each inventory category. 

To approximate the sales of trucks in future years, and thus the impact to the age 

distribution of trucks, staff first estimated the historic annual nationwide truck sales as a 

function of the historic nationwide GDP associated with transportation activities. Staff 

used data regarding nationwide truck sales, as estimated by the website 

WardsAuto.com for 1985-2007, and GDP, as estimated for transportation services 

nationwide by the US Department of Commerce’s Bureau of Economic Analysis to 

establish the historical model of truck sales as a function of time and the nationwide 

transportation GDP. The approximation of this model indicated that GDP had a 

stronger positive correlation with truck sales than did time itself; while truck sales had a 

positive correlation with time itself, the correlation that also existed between GDP and 

time lessened the positive impact of time when truck sales were regressed against both. 

The strongest relationship was also found to exist between truck sales in Year y and the 

GDP from Year y+1, the time lag indicating that the trucking industry could foresee the 

decline in economic activity and deferred the purchase of new trucks before the decline 

was experienced. 
G-48 

Staff then approximated the historic relationship between the historic nationwide 

transportation GDP and the historic California transportation employment, as estimated 

by a UCLA business forecast released in July 2007 (UCLA, 2007). We assumed that 

the growth rate for transportation employment in California was approximated by the 

growth rate for nationwide transportation GDP. We used this relationship with the 

projections to 2020 for California transportation employment from the UCLA forecast to 

project the nationwide transportation GDP until 2020 as shown in Figure 20. 

Figure 20. Nationwide Transportation GDP Trends: Historic and as Projected 

from Forecast Transportation Employment 

 

0

50

100

150

200

250

300

350

400

450

500

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Nationwide Transportation GDP 

<-- Actual

Projected -->
G-49 

As a first step toward projecting truck sales, staff developed a regression model 

between nationwide transportation-related GDP and historic truck sales. We then used 

the estimates of the future transportation–related GDP to estimate future truck sales. In 

Figure 21 we compare the actual truck sales between 1985 and 2006 with the sales that 

would have been predicted by the model as well as the truck sales initially predicted 

from this model beyond 2006. 

Figure 21. The Modeled Relationship between Nationwide Transportation GDP 

and Nationwide Truck Sales 

 

0

50,000

100,000

150,000

200,000

250,000

300,000

1985 1990 1995 2000 2005 2010 2015

Truck Sales

Actual truck sales

Predicted truck 

sales
G-50 

The modeled relationship did not account for what appeared to be extremes. During 

periods of slow sales, the model underpredicted the decrease in sales; during periods of 

higher sales, the model underpredicted the increase in sales. To account for this, staff 

estimated a correction factor by which the 2008 truck sales were lowered. This 

correction factor, the adjustment made to the estimate resulting from the model, was 

equivalent in proportion to the difference between earlier estimates and corresponding 

actual results. Staff then adjusted truck sales between 2008 and 2015 by an amount 

appropriate to ensure that the aggregate number of trucks sold within this period 

remained constant. We show the resulting estimate of truck sales, the “adjusted truck 

sales”, in Figure 22. 

Figure 22. The Modeled Relationship between Nationwide Transportation GDP 

and Nationwide Truck Sales, Adjusted 

0

50,000

100,000

150,000

200,000

250,000

300,000

1985 1990 1995 2000 2005 2010 2015 2020

Truck Sales

Actual truck sales

Predicted truck 

sales

Adjusted truck 

sales

Transportation employment within California was already projected to grow at a slightly 

slower rate after 2015; hence sales for this time period were projected to grow at a 

slower rate. 
G-51 

Finally, staff received additional data regarding truck sales to date in 2008. These sales 

were even lower than those projected by the adjusted model. To account for this 

difference, staff adjusted the model even further, decreasing the sales projected for 

2008 and adjusting those in future years to account for the difference – sales in 

between 2008 and 2011 were adjusted downward, sales after 2011 were unchanged. 

We show the results of these adjustments in Figure 23. 

Figure 23. (Estimated) Truck Sales for Future Years 

0

50,000

100,000

150,000

200,000

250,000

300,000

1985 1990 1995 2000 2005 2010 2015 2020

Truck Sales

Actual 

truck 

sales

Originally predicted 

truck sales

Predicted truck 

sales after first 

adjustment

Predicted truck 

sales after 

second 

adjustment

Because no data were available at the time for prediction of GDP past 2020 with the 

model used in this analysis, we assumed that trucks were sold in the years beyond 

2020 at the same ratio as has happened historically. 
G-52 

Finally, to incorporate these estimates in the populations, staff calculated the ratio 

between sales of trucks in one model year and the expected number of sales if no factor 

(e.g. GDP) had caused variation. We show the actual sales and the sales predicted 

from the pre-buy model in Figure 24. 

Figure 24. Actual and Predicted Truck Sales 

Best-fit line

Truck Sales = 

4.46*10-12e

0.019E-02*year

0

50,000

100,000

150,000

200,000

250,000

300,000

1985 1990 1995 2000 2005 2010 2015 2020

Truck Sales

Actual and 

Predicted 

Truck Sales

The ratio between the actual/predicted truck sales and the best-fit value (the ratio 

assumed to be 1.0 for future years beyond 2019) was used to adjust the representation 

of each model year within future calendar years. (Adjusting the sales upward/downward 

for new trucks in a calendar year would have the same impact on the representation of 

trucks of that model year in later calendar years.) The representation of each model 

year was then scaled as appropriate to allow the cumulative representation of model 

years to be 100%. This adjustment was not made for the age distribution of drayage 

trucks, since the age distribution from the ARB drayage regulation was used. 
G-53 

G. Statewide Baseline Activity and Emissions 

We discussed the population of each vehicle type earlier in the Base Year Population 

and Activity section, but we provide a summary table (Table 22) here to describe the 

emission sources included in this inventory. 

Table 22. Population and VMT by Inventory Category

CY Category Population Share of CA 

Population 

CA Share 

of VMT 

Share of 

VMT in CA 

2008 HH Out-of-State 492,340 52.3% 12.1% 28.5% 

2008 HH CA-IRP 60,263 6.4% 57.0% 14.6% 

2008 HH Tractor 63,684 6.8% 100.0% 18.4% 

2008 HH Single Unit 43,275 4.6% 100.0% 6.0% 

2008 HH Drayage 21,650 2.3% 100.0% 5.1% 

2008 HH Agriculture 11,998 1.3% 100.0% 1.6% 

2008 HH Utility 1,357 0.1% 100.0% 0.1% 

2008 MH In-State 198,525 21.1% 100.0% 22.5% 

2008 MH Interstate 8,896 0.9% 18.7% 0.2% 

2008 MH Agriculture 9,438 1.0% 100.0% 0.6% 

2008 MH Utility 2,798 0.3% 100.0% 0.1% 

2008 Buses 26,443 2.8% 100.0% 2.3% 

2008 PTO 

For this table and later analyses, some categories have been combined to allow for 

simpler evaluation. Specifically, HH Out-of-State trucks include those trucks from 

neighboring states as well as non-neighboring states. HH Drayage trucks includes all 

drayage trucks in service in California, including those serving areas around the Ports of 

Los Angeles, Long Beach, Oakland, and other California ports. MH Interstate trucks 

include the medium-heavy trucks registered in other states as well as those registered 

in the CA-IRP program. Buses include those in the “school bus” and “other bus” 

inventory categories. 

The population shown in Table 22 represents the estimated number of trucks and buses 

operating in California that fall under this proposed rule in calendar year 2008, including 

vehicles registered in-state and out-of-state. The share of California population 

represents the percentage of the total truck and bus population represented by each 

category. The California share of VMT represents the percentage of total annual VMT 

driven by vehicles in each category that occurs in California; this is assumed to be 

100% for the majority of inventory categories but can be small for interstate categories. 

The share of VMT in California represents the contribution made by each inventory 

category to the total truck and bus VMT estimated for California. 

As shown in Table 22, heavy-heavy duty diesel trucks registered outside California 

account for the majority of trucks traveling in California. Because these trucks do not 

travel exclusively in California, however, their portion of VMT in California, at 28.5%, is 

not as large as their representation among the population (52.3%). In addition, because 

trucks in this inventory category tend to be newer than trucks in other categories, their 
G-54 

emission rates are lower and their contribution relatively less than from their in-state 

counterparts. 

1. Base Year Age Distributions across Inventory Category 

In an earlier section, we provided the age distribution of trucks within each inventory 

category. In Figure 25 we compare the age distributions for the different categories. 

(Note that trucks from non-neighboring states, because they outnumber trucks from 

other categories by such a large share, are plotted against the secondary axis.) From 

this figure one can see from the relative peaks of each age distribution that trucks from 

non-neighboring states tend to be newer than CA-IRP registered trucks and trucks from 

neighboring states, which in turn are newer than tractors registered instate. This is 

logical, in that the trucks traveling the farthest distances to California need to be the 

most reliable and therefore on average younger. In addition, the trucks representing the 

CA instate tractor category are frequently those transferred from the out-of-state and 

IRP categories in their later years. The oldest trucks represent the agriculture category 

and the drayage category; these trucks tend to travel, on average, the shortest 

distances. Staff used this age distribution when estimating emissions. 

Figure 25. Comparison of Vehicle Age Distributions between Combination 

Tractors (2008) 

HH

Drayage

HH CA IRP

HH Instate

 HH

NOOS

HH Ag

HH NNOOS

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Population (NNOOS)

HH Drayage HH CA IRP HH Instate Tractor

HH NOOS HH Agriculture HH NNOOS Trucks

Figure 26 gives a similar representation of the trucking categories more representative 

of single unit trucks. (In this figure, the MHDDT Instate Trucks are plotted on the 

secondary axis to allow for better comparison.) As this figure shows, the age 
G-55 

distribution for single-unit trucks does not vary so much between categories as the age 

distribution does for combination trucks. With regard to single-unit categories in 

particular, medium-heavy instate trucks are relatively newer and medium-heavy 

agriculture trucks relatively older. 

Figure 26. Comparison of Vehicle Age Distributions between Single-Unit Trucks 

and Buses (2008) 

HH Single Unit

MH Ut

HH Ut

MH Ag

Buses

MH Instate

0

500

1,000

1,500

2,000

2,500

3,000

3,500

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

Population

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

Population (MH Instate)

HH Single Unit Trucks MH Utility Trucks HH Utility Trucks

MH Agriculture Buses MH Instate Trucks
G-56 

2. Base Year and Forecasted Population by Inventory Category 

In Figure 27 we show the distribution of trucks assumed to visit California in 2008 and 

2020. In both years the majority of individual trucks represent one of four categories: 

heavy-heavy trucks registered outside California, California-registered heavy-heavy 

trucks in the International Registration Program, heavy-heavy tractors registered in 

California, and medium-heavy trucks registered in California. In a later section we show 

the distribution of California VMT between the categories; for two primary reasons, the 

distribution of VMT differs slightly from the distribution of the unique trucks themselves. 

First, trucks registered outside California do not travel entirely within California. 

Second, trucks within differing categories typically do not exhibit the same travel 

patterns with regard to distance. 

Figure 27. A Comparison of Truck Population by Category (2008 and 2020) 

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

2008 2020

Truck count

PTO

Buses

MH Utility

MH Agriculture

MH Interstate

MH In-State

HH Utility

HH Agriculture

HH Drayage

HH Singleunit

HH Tractor

HH CA-IRP

HH Out-of-State

HH Out-of-State HH Out-of-State

HH CA-IRP

HH CA-IRP

HH Tractor (Instate)

MH In-state HH Tractor (Instate)

MH In-state
G-57 

3. Comparing Accrual Rates by Inventory Category 

In Figure 28 we show the variation in VMT accrual rates between inventory categories 

in California. Note that this figure shows only the VMT estimated to be accrued within 

California’s borders and does not include the VMT estimated to be accrued elsewhere. 

This is particularly relevant in the context of trucks registered in states that do not 

border California, of which more than 90% of the annual VMT are assumed to be 

accrued outside California. In the context of virtually all heavy vehicles except drayage 

trucks, the annual VMT for a vehicle is estimated to decrease with the age of the 

vehicle; drayage trucks are assumed to maintain a steady amount of usage throughout 

their lives. 

Figure 28. Accrual Rates for Combination Trucks in California (2008) 

HH CA IRP

HH NNOOS

HH NOOS

HH Ag

Other Ports

 HH Instate

HH POAK Trucks

HH POLA\LB

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

CA Accrual (miles)

HH CA IRP HH Instate Tractor HH NNOOS Trucks

HH NOOS HH Agriculture Other Ports Trucks

HH POAK Trucks HH POLA\LB Trucks
G-58 

In Figure 29 we show a similar distribution in accrual rates for single unit trucks. As 

shown, the average accrual rate is estimated to be highest for single unit trucks. The 

accrual rate is estimated to decrease with age for single unit trucks as well as mediumheavy instate trucks, buses, and medium-heavy trucks used for agriculture. The accrual 

rates estimated for heavy-heavy trucks from the utility category, however, start at a 

lower level and remain more stable throughout their lifetimes. 

Figure 29. Accrual Rates for Single Unit Trucks in California (2008) 

HH Single Unit

MH Utility Trucks

HH Utility Trucks

MH Instate

MH Agriculture

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Age

CA Accrual (miles)

HH Single Unit Trucks MH Utility Trucks HH Utility Trucks

MH Instate Trucks MH Agriculture

4. Base Year and Forecasted Vehicle Miles Traveled by Inventory Category 

In Figure 30 and Table 23 we show the distribution of VMT driven by trucks in different 

categories in California between calendar years 2005 and 2025. Figure 30 shows the 

aggregate VMT for each year; the table lists the daily VMT for each truck category in 

select years. The five most significant contributors to VMT driven in California in 

calendar year 2008 are: 

• MHDDT CA-registered instate trucks (22.5%) 

• HHDDT Non-neighboring out-of-state trucks (21.5%) 

• HHDDT CA-registered instate tractor (18.4%) 

• HHDDT International Registration Plan, CA-IRP (14.6%) 

• HHDDT Neighboring out-of-state trucks (7.0%) 
G-59 

Staff estimated these five categories together represent over 81% of all VMT associated 

with bus and truck travel in California in 2008. Though not necessarily in the same 

order, we project these five categories to remain the largest heavy duty truck VMT 

contributors in 2025, collectively accounting for over 83% of all heavy duty truck VMT 

driven within California’s borders. (The HHDDT from states not neighboring California 

are projected to represent the single largest share in 2025 at 22.1%.) 

Figure 30. California Vehicle Miles Traveled by Truck Category 

0

10

20

30

40

50

60

70

80

90

200520062007200820092010201120122013201420152016201720182019202020212022202320242025

VMT (millions/day)

PTO

Buses

MH Utility

MH Agriculture

MH Interstate

MH In-State

HH Utility

HH Agriculture

HH Drayage

HH Single

HH Tractor

HH CA-IRP

HH Out-of-State

HH Instate Tractor

HH Singleunit

HH Out of State

HH International Registration Plan

MH In-state

HH Drayage
G-60 

Table 23. Distribution of Estimated Daily VMT in Select Calendar Years

Inventory 

Category 2000 2005 2010 2015 2020 2025 

HH Out-of-State 11,763,164 14,094,931 16,694,413 19,776,058 22,554,003 24,722,681

HH CA-IRP 6,035,750 7,232,194 8,566,004 10,147,215 11,572,595 12,685,357

HH Tractor 7,588,662 9,092,933 10,769,913 12,757,947 14,550,057 15,949,116

HH Single 2,485,547 2,978,247 3,527,515 4,178,664 4,765,642 5,223,882 

HH Drayage 1,992,437 2,501,164 3,177,789 4,017,716 5,063,470 6,407,181 

HH Agriculture 900,850 886,807 872,982 859,373 845,976 832,788 

HH Utility 28,849 31,852 35,167 38,827 42,868 47,330 

MH In-State 10,188,740 11,871,644 13,287,487 14,478,516 15,531,621 16,717,215

MH Interstate 94,153 109,705 122,789 133,795 143,526 154,482 

MH Agriculture 351,375 345,897 340,505 335,197 329,972 324,828 

MH Utility 47,746 52,716 58,203 64,260 70,949 78,333 

Buses 890,204 1,125,836 1,259,711 1,371,993 1,500,120 1,617,738 

PTO 358,622 429,614 509,088 601,588 685,075 751,094 

Total 42,726,100 50,753,540 59,221,565 68,761,149 77,655,874 85,512,024

In Figure 31 we show the percentage of VMT associated with each category between 

2005 and 2025. As the figure shows, the share of VMT represented by drayage traffic 

is expected to grow at the expense of other categories (e.g. MHDDT instate) due to the 

relatively higher growth in goods movement related truck categories. 

Figure 31. Share of California Vehicle Miles Traveled by Truck Category 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

200520062007200820092010201120122013201420152016201720182019202020212022202320242025

Daily VMT Share

PTO

Buses

MH Utility

MH Agriculture

MH Interstate

MH In-State

HH Utility

HH Agriculture

HH Drayage

HH Single

HH Tractor

HH CA-IRP

HH Out-of-State

HH Instate Tractor

HH Singleunit

HH Out of State

HH International Registration Plan

MH In-state 

HH Drayage
G-61 

Table 24 provides the estimated population, average age, average odometer reading, 

and total VMT for each category of heavy duty truck in calendar year 2008. 

Table 24. Assumptions made regarding truck categories in calculations. 

CY Inventory 

Category Population Average Age 

Average 

Odometer Total Accrual 

2008 HH Out-of-State 492,340 3.8 489,526 41,666,633,775 

2008 HH CA-IRP 60,263 6.2 668,314 4,535,889,834 

2008 HH Tractor 63,684 9.9 722,999 3,249,093,053 

2008 HH Single unit 43,275 10.3 338,253 1,064,186,055 

2008 HH Drayage 21,650 11.8 839,789 904,462,366 

2008 HH Agriculture 11,998 17.0 601,454 273,775,114 

2008 HH Utility 1,357 8.2 74,611 10,545,996 

2008 MH In-State 198,525 8.0 206,852 3,972,137,620 

2008 MH Interstate 8,896 5.4 161,306 196,581,895 

2008 MH Agriculture 9,438 17.3 293,027 106,907,874 

2008 MH Utility 2,798 7.2 56,377 17,455,166 

2008 Buses 26,443 11.2 191,829 406,667,394 

2008 PTO 
G-62 

In Figure 32 we show the distribution of annual truck VMT graphically. Four categories 

again represent the majority of truck VMT: heavy-heavy tractors registered outside 

California, heavy-heavy tractors registered in the International Registration Program, 

heavy-heavy tractors registered in California, and medium-heavy trucks registered in 

California. 

Figure 32. A Comparison of Annual Truck VMT by Category (2008 and 2020) 

0

5

10

15

20

25

30

2008 2020

Annual Truck VMT (Billions)

PTO

Buses

MH Utility

MH Agriculture

MH Interstate

MH In-State

HH Utility

HH Agriculture

HH Drayage

HH Singleunit

HH Tractor

HH CA-IRP

HH Out-of-State

5. Statewide Baseline Emissions Estimates 

a) Baseline Emissions 

Prior to implementation of the proposed regulation, approximately 941,000 trucks and 

buses were operating in California in calendar year 2008 and are estimated to 

contribute 859.3 tons per day NOx, 33.1 tons per day PM2.5, and 108,429 tons per day 

CO2. We show these data in Table 25. 

Table 25. Statewide Emissions (2008) 

CY Category Truck 

Population

Truck CA 

VMT/day 

NOx

(tons/day)

PM2.5

(tons/day)

CO2

(tons/day)

2008 All 940,667 57,009,437 859.3 33.1 108,429 

HH Out-of-State HH Out-of-State

HH CA-IRP

HH CA-IRP

HH Tractor (Instate)

HH Tractor (Instate)

MH In-state

MH In-state
G-63 

b) Baseline Emissions by Inventory Category 

We provide in Table 26 the baseline heavy duty truck and bus emissions inventory for 

the entire state of California in 2008, broken down by inventory category. 

Table 26. Statewide Emissions by Inventory Category (2008) 

CY Inventory 

Category 

Truck 

Population

Truck CA 

VMT/day 

NOx

(tons/day) 

PM2.5

(tons/day) 

CO2

(tons/day) 

2008 HH Out-of-State 492,340 16,142,326 221.9 8.0 32,766 

2008 HH CA-IRP 60,263 8,282,725 139.6 5.2 16,783 

2008 HH Tractor 63,684 10,413,751 194.1 7.9 20,897 

2008 HH Single-unit 43,275 3,410,860 57.8 1.9 6,876

2008 HH Drayage 21,650 2,898,907 70.0 3.2 6,006 

2008 HH Agriculture 11,998 878,486 17.3 0.7 1,788 

2008 HH Utility 1,357 33,801 1 0 79 

2008 MH In-State 198,525 12,731,247 125.0 4.6 19,067 

2008 MH Interstate 8,896 117,648 1.0 0.0 176 

2008 MH Agriculture 9,438 342,652 4.0 0.2 521 

2008 MH Utility 2,798 55,942 1 0 85 

2008 Buses 26,443 1,208,769 15.0 0.4 2,036 

2008 PTO 0 492,322 12.2 0.8 1,349 

2008 All 940,667 57,009,437 859.3 33.1 108,429 

c) Baseline Emissions by Fleet Size 

The baseline heavy duty truck emissions inventory for the entire state of California, 

broken down by inventory category, fleet size, and mileage threshold, is shown in Table 

27. 

Table 27. Baseline Emissions by Inventory Category and Fleet Size (2008) 

Inventory 

Category Fleet Size Truck Population 

Truck CA 

VMT/day 

NOx

(tons/day) 

PM2.5

(tons/day)

CO2

(tons/day) 

1 truck/above 7500 miles 12,011 694,474 10.92 0.40 1,411.3 

1 truck/below 7500 miles 432 1,337 0.04 0.00 4.2 

2 truck/above 7500 miles 3,695 214,735 3.38 0.12 436.4 

2 truck/below 7500 miles 133 414 0.01 0.00 1.3 

3 truck/above 7500 miles 2,652 155,555 2.45 0.09 316.1 

3 truck/below 7500 miles 96 301 0.01 0.00 1.0 

4+ truck/above 7500 miles 460,630 15,056,647 204.54 7.36 30,535.2 

HH Out of State 

4+ truck/below 7500 miles 12,690 18,863 0.56 0.02 60.5 

1 truck/above 7500 miles 15,980 2,198,998 41.85 1.49 4,470.2 

1 truck/below 7500 miles 883 5,804 0.17 0.01 17.0 

2 truck/above 7500 miles 5,850 812,823 15.01 0.54 1,649.9 

2 truck/below 7500 miles 318 2,096 0.06 0.00 6.1 

3 truck/above 7500 miles 3,297 460,037 8.28 0.31 933.0 

3 truck/below 7500 miles 183 1,223 0.04 0.00 3.6 

4+ truck/above 7500 miles 32,273 4,791,942 73.95 2.83 9,674.7 

HH CAIRP 

4+ truck/below 7500 miles 1,479 9,801 0.28 0.01 28.7 

1 truck/above 7500 miles 16,792 3,031,868 65.83 2.71 6,110.9 

1 truck/below 7500 miles 4,089 44,925 1.16 0.07 99.4 

2 truck/above 7500 miles 4,928 902,069 18.70 0.80 1,815.4 

HH Tractor 

2 truck/below 7500 miles 1,189 12,992 0.33 0.02 28.8 
G-64 

Inventory 

Category Fleet Size Truck Population 

Truck CA 

VMT/day 

NOx

(tons/day) 

PM2.5

(tons/day)

CO2

(tons/day) 

3 truck/above 7500 miles 2,876 529,836 10.87 0.46 1,066.2 

3 truck/below 7500 miles 673 7,370 0.19 0.01 16.3 

4+ truck/above 7500 miles 28,859 5,835,899 95.82 3.82 11,652.5 

4+ truck/below 7500 miles 4,278 48,792 1.20 0.07 107.2 

1 truck/above 7500 miles 7,819 766,081 14.15 0.49 1,545.2 

1 truck/below 7500 miles 3,358 37,585 0.88 0.04 82.8 

2 truck/above 7500 miles 2,826 279,769 5.01 0.18 563.7 

2 truck/below 7500 miles 1,171 13,153 0.31 0.01 29.0 

3 truck/above 7500 miles 1,857 185,491 3.26 0.11 373.0 

3 truck/below 7500 miles 703 7,951 0.18 0.01 17.4 

4+ truck/above 7500 miles 20,001 2,057,340 32.54 1.03 4,125.9 

HH Single Unit 

4+ truck/below 7500 miles 5,540 63,491 1.44 0.06 138.9 

1 truck 11,633 1,557,622 37.64 1.69 3,227.1 

2 truck 1,293 173,069 4.18 0.19 358.6 

3 truck 1,454 194,703 4.70 0.21 403.4 HH Drayage 

4+ truck 7,270 973,513 23.52 1.06 2,017.0 

Ag non specialty higher VMT 4,098 645,375 12.41 0.49 1,301.8 

Ag non specialty lower VMT 5,258 82,489 1.89 0.10 178.5 

Ag non specialty midrange VMT 1,442 62,773 1.27 0.06 128.9 HH Agriculture 

Ag specialty vehicle 1,200 87,849 1.73 0.07 178.8 

HH Utility 1,357 33,801 0.74 0.01 79.3 

1 truck/above 5000 miles 51,066 3,786,410 39.01 1.48 5,672.2 

1 truck/below 5000 miles 11,592 80,942 1.20 0.06 127.6 

2 truck/above 5000 miles 16,710 1,244,223 12.65 0.47 1,862.8 

2 truck/below 5000 miles 3,621 25,478 0.37 0.02 40.1 

3 truck/above 5000 miles 9,964 740,914 7.59 0.28 1,109.6 

3 truck/below 5000 miles 2,143 15,111 0.22 0.01 23.8 

4+ truck/above 5000 miles 88,520 6,731,027 62.45 2.24 10,063.3 

MH instate 

4+ truck/below 5000 miles 14,908 107,142 1.50 0.07 167.8 

1 truck/above 5000 miles 1,014 15,237 0.13 0.00 22.7 

1 truck/below 5000 miles 107 149 0.00 0.00 0.2 

2 truck/above 5000 miles 638 9,468 0.08 0.00 14.1 

2 truck/below 5000 miles 70 97 0.00 0.00 0.2 

3 truck/above 5000 miles 638 9,351 0.08 0.00 14.0 

3 truck/below 5000 miles 77 106 0.00 0.00 0.2 

4+ truck/above 5000 miles 5,648 82,272 0.70 0.02 122.7 

MH Interstate 

4+ truck/below 5000 miles 703 969 0.01 0.00 1.5 

Ag non specialty higher VMT 1,750 183,598 2.04 0.09 277.0 

Ag non specialty lower VMT 4,799 46,021 0.66 0.04 72.6 

Ag non specialty midrange VMT 1,946 78,768 0.89 0.04 119.5 MH Agriculture 

Ag specialty vehicle 944 34,265 0.40 0.02 52.1 

MH Utility 2,798 55,942 0.54 0.01 85.4 

Buses 26,443 1,208,769 15.04 0.44 2,035.8 

PTO 492,322 12.19 0.83 1,348.7 

All 940,667 57,009,437 859.28 33.07 108,429.2 
G-65 

We show in Table 28 the baseline emissions inventory for the entire state of California, 

broken down only by fleet size and annual mileage thresholds. 

Table 28. Baseline Emissions by Fleet Size and Mileage Threshold (2008) 

CY Fleet Size Truck 

Population 

Truck CA 

VMT/day 

NOx

(tons/day)

PM2.5

(tons/day)

CO2

(tons/day)

2008 1 truck/above threshold 116,314 12,050,689 209.5 8.26 22,460 

2008 1 truck/below threshold 20,462 170,742 3.4 0.17 331 

2008 2 truck/above threshold 35,941 3,636,157 59.0 2.30 6,701 

2008 2 truck/below threshold 6,502 54,229 1.1 0.05 105 

2008 3 truck/above threshold 22,739 2,275,887 37.2 1.46 4,215 

2008 3 truck/below threshold 3,876 32,062 0.6 0.03 62 

2008 4+ truck/above threshold 643,202 35,528,640 493.5 18.36 68,191 

2008 4+ truck/below threshold 39,598 249,058 5.0 0.22 505 

2008 Ag non specialty higher VMT 5,848 828,973 14.5 0.58 1,579 

2008 Ag non specialty lower VMT 10,057 128,510 2.6 0.14 251 

2008 Ag non specialty midrange VMT 3,388 141,541 2.2 0.10 248 

2008 Ag specialty vehicle 2,144 122,114 2.1 0.09 231 

2008 Unspecified 30,597 1,790,835 28.5 1.29 3,549 

2008 All 940,667 57,009,437 859.3 33.07 108,429 
G-66 

We show the emissions as estimated with the baseline scenario for each category of 

pollutant in the following figures: NOx in Figure 33, PM2.5 in Figure 34, and CO2 in Figure 

35. As shown, the statewide emissions for NOx and PM2.5 are expected to decrease in 

the absence of regulation, due to the natural replacement of older trucks with newer, 

cleaner trucks. Baseline CO2 emissions, however, are projected to increase since 

improvements in fuel economy are not expected to keep pace with increased heavy 

duty truck VMT. ARB is proposing to improve fuel economy and reduce CO2 emissions 

from heavy duty trucks in future years through other programs and technologies, 

including increased usage of aerodynamic fairings and tires of lower rolling resistance. 

Figure 33. California Statewide NOx emissions from Trucks, Baseline 2008 - 2023 

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Figure 34. California Statewide PM2.5 Emissions from Trucks, 2008-2023 

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Figure 35. California Statewide CO2 Emissions from Trucks, Baseline 2008-2023 

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G-68 

H. Statewide Truck and Bus Regulation Benefits 

The proposed Statewide Truck and Bus Regulation would initially require older vehicles 

to install diesel particulate filters. Several years later, the regulation would require 

operators to either purchase a newer compliant vehicle to replace an older noncompliant vehicle or retrofit the older non-compliant vehicle with emissions controls that 

would enable each vehicle to comply with regulatory emission standards. Under the 

proposed regulation, a fleet operator may choose among multiple compliance options 

on a per-pollutant and per-calendar year basis. Options include: (1) best available 

control technology (BACT) schedule; (2) fleet average requirements; (3) percent limit 

requirements; (4) low mileage thresholds with alternative compliance schedules; (5) 

small fleet compliance options; (6) regional compliance options; and (7) other special 

provisions. 

To estimate the emissions benefits generated by the proposed regulation, one must 

understand how individual fleet operators may choose to comply with the regulation. 

Each operator’s response may ultimately depend on the age, body type, and other 

characteristics of vehicles in each fleet and the relationship between vehicles in each 

fleet to the inventory categories. One of the ways we assess potential compliance 

patterns is by evaluating previous vehicle buying patterns by fleets based on survey 

data collected by staff. We input this information to the cost-model developed to assess 

capital costs under the baseline scenario and the scenario with regulation. We based 

the cost and economic model, described in greater detail in the Appendix on Cost and 

Economic Analysis Methodology upon survey data representing 6,700 vehicles from 

688 individual company fleets. 

Another way we can assess potential compliance patterns is by evaluating the base 

year age distribution in the inventory by category. As discussed above, the age 

distribution of non-neighboring out-of-state trucks suggests that trucks engaged in the 

longest hauls tend to be purchased new. After several years in long-haul operation, 

trucks tend to be sold to regional fleets, and a few years after that to local fleets. This 

type of purchase behavior may not be entirely true of all fleets or trucks within an 

inventory category, but it likely is representative of the majority. In addition to evaluating 

the age distribution for each inventory category, we also evaluated age distributions for 

each fleet size and mileage threshold group within each inventory category. 

Our benefit calculations are also fundamentally based on the idea that newer vehicles 

drive more than older vehicles, but that the regulation will not affect the number of 

vehicle miles traveled within California. As a result, as new vehicles are purchased due 

to regulatory requirements, we redistribute VMT across age distributions by inventory 

category to ensure VMT is conserved, and to ensure that newer vehicles continue to be 

driven more than older vehicles. 

1. Methodology to Assess Statewide Benefits 

To calculate emission benefits, we used a methodology that separated vehicles into five 

groups: (a) high mileage large fleets, (b) high mileage small fleets, (c) low mileage 
G-69 

vehicles in large or small fleets, (d) agricultural trucks, and (e) buses. For each group 

we developed a compliance schedule based upon our best estimate of anticipated 

purchase decisions. Our compliance schedules developed for each inventory category 

and compliance group assumed that larger, newer fleets will comply with the regulation 

by purchasing new or near-new vehicles and that where possible fleets will choose to 

avoid installing retrofits (especially on mechanically controlled engines that are more 

costly to control) – instead opting to purchase 2007 standard compliant trucks. 

a) High Mileage Trucks in Fleets of Four or Greater

Large, high-mileage truck fleets are well-represented in the cost model. The cost model 

uses previous purchase behavior to predict future purchases for regulatory compliance 

by fleet, based on the compliance schedules available to these fleets. We assigned an 

inventory category to each truck in each fleet so that model results could be 

summarized into four general categories: in-state heavy-heavy tractors, in-state heavyheavy single-unit trucks, medium heavy duty trucks, and heavy-heavy interstate trucks. 

We then compiled and analyzed model results to develop a compliance schedule for the 

four categories. 
G-70 

We show those compliance schedules in Table 29. As shown in the table, we assume 

large fleets will choose to replace, rather than retrofit, pre-2003 model year vehicles to 

meet PM BACT requirements. We do assume fleets will choose to retrofit 2003-2006 

model year trucks in order to gain maximum use out of recently purchased vehicles. 

We also assume that, as in-state fleets purchase compliant replacement vehicles, they 

will purchase a vehicle that is four or five year old. This assumption is a schematic 

representation that some fleets will choose to purchase older compliant vehicles 

relatively cheaply, while other fleets will choose to purchase new or near-new vehicles. 

Table 29 shows that interstate fleets will replace trucks with newer trucks more 

frequently than in-state fleets. 

Table 29. Compliance Assumptions for High-Mileage Trucks in Fleets of Four or 

Greater 

Heavy-Heavy Duty Diesel In-state Tractors 

 Turnover 

As of 

January 1, 

Model 

Year Percent Calendar Year Percent Calendar Year Percent Calendar Year 

Percent 

with DPF 

(85% 

Control) 

2011 pre-1994 95.5% 2008 4.5% 2010 0.0% 2012 

2012 pre-1994 95.5% 2008 4.5% 2010 0.0% 2012 

2012 2003-2004 100% 

2013 pre-2000 59.5% 2008 35.8% 2010 4.7% 2012 

2013 2005-2006 100% 

2014 pre-2003 45.2% 2008 30.1% 2010 24.7% 2012 

2015 pre-2003 45.2% 2008 30.1% 2010 24.7% 2012 

2016 pre-2005 41.2% 2008 26.6% 2010 32.2% 2012 

2017 pre-2007 37.1% 2008 24.0% 2010 38.8% 2012 

2018 pre-2007 37.1% 2008 0.0% 2010 62.9% 2013 

2019 pre-2007 37.1% 2008 0.0% 2010 62.9% 2014 

2020 pre-2007 37.1% 2008 0.0% 2010 62.9% 2015 

2021 pre-2008 21.7% 2008 0.0% 2010 78.3% 2016 

2022 pre-2009 0.0% 2008 0.0% 2010 100.0% 2016 

2023 pre-2010 0.0% 2008 0.0% 2010 100.0% 2017 

2024 pre-2010 0.0% 2008 0.0% 2010 100.0% 2018 

2025 pre-2010 0.0% 2008 0.0% 2010 100.0% 2019 
G-71 

Heavy-Heavy Duty Diesel In-State Single-Units and Power-Take Off 

Turnover 

As of 

January 1, 

Model 

Year Percent Calendar Year Percent Calendar Year Percent Calendar Year 

Percent 

with DPF 

(85% 

Control) 

2011 pre-1994 100.0% 2008 0.0% 2010 0.0% 2012 

2012 pre-1994 100.0% 2008 0.0% 2010 0.0% 2012 

2012 2003-2004 100% 

2013 pre-2000 61.1% 2008 37.0% 2010 1.9% 2012 

2013 2005-2006 100% 

2014 pre-2003 47.9% 2008 37.4% 2010 14.7% 2012 

2015 pre-2003 47.9% 2008 37.4% 2010 14.7% 2012 

2016 pre-2005 44.8% 2008 33.9% 2010 21.3% 2012 

2017 pre-2007 38.5% 2008 30.5% 2010 31.0% 2012 

2018 pre-2007 38.5% 2008 0.0% 2010 61.5% 2013 

2019 pre-2007 38.5% 2008 0.0% 2010 61.5% 2014 

2020 pre-2007 38.5% 2008 0.0% 2010 61.5% 2015 

2021 pre-2008 22.6% 2008 0.0% 2010 77.4% 2016 

2022 pre-2009 0.0% 2008 0.0% 2010 100.0% 2016 

2023 pre-2010 0.0% 2008 0.0% 2010 100.0% 2017 

2024 pre-2010 0.0% 2008 0.0% 2010 100.0% 2018 

2025 pre-2010 0.0% 2008 0.0% 2010 100.0% 2019 

Medium-Heavy-Duty In-State and Interstate 

Turnover 

As of 

January 1, 

Model 

Year Percent Calendar Year Percent Calendar Year Percent Calendar Year 

Percent 

with DPF 

(85% 

Control) 

2011 pre-1994 93.3% 2008 6.7% 2010 0.0% 2012 

2012 pre-1994 93.3% 2008 6.7% 2010 0.0% 2012 

2012 

2003-

2004 100% 

2013 pre-2000 72.5% 2008 27.0% 2010 0.5% 2012 

2013 

2005-

2006 100% 

2014 pre-2003 56.4% 2008 25.5% 2010 18.2% 2012 

2015 pre-2003 56.4% 2008 25.5% 2010 18.2% 2012 

2016 pre-2005 49.8% 2008 21.5% 2010 28.6% 2012 

2017 pre-2007 47.9% 2008 20.7% 2010 31.4% 2012 

2018 pre-2007 47.9% 2008 0.0% 2010 52.1% 2013 

2019 pre-2007 47.9% 2008 0.0% 2010 52.1% 2014 

2020 pre-2007 47.9% 2008 0.0% 2010 52.1% 2015 

2021 pre-2008 28.8% 2008 0.0% 2010 71.2% 2016 

2022 pre-2009 0.0% 2008 0.0% 2010 100.0% 2016 

2023 pre-2010 0.0% 2008 0.0% 2010 100.0% 2017 

2024 pre-2010 0.0% 2008 0.0% 2010 100.0% 2018 

2025 pre-2010 0.0% 2008 0.0% 2010 100.0% 2019 
G-72 

Heavy-Heavy Duty Interstate Trucks 

Turnover 

As of 

January 1, 

Model 

Year Percent Calendar Year Percent Calendar Year Percent Calendar Year 

Percent 

with DPF 

(85% 

Control) 

2011 pre-1994 85.4% 2008 14.6% 2010 0.0% 2012 

2012 pre-1994 85.4% 2008 14.6% 2010 0.0% 2012 

2012 2003-2004 100% 

2013 pre-2000 48.8% 2008 42.9% 2010 8.3% 2012 

2013 2005-2006 100% 

2014 pre-2003 23.0% 2008 26.8% 2010 50.2% 2012 

2015 pre-2003 23.0% 2008 26.8% 2010 50.2% 2012 

2016 pre-2005 20.4% 2008 23.8% 2010 55.8% 2012 

2017 pre-2007 19.2% 2008 22.4% 2010 58.4% 2012 

2018 pre-2007 19.2% 2008 0.0% 2010 80.8% 2013 

2019 pre-2007 19.2% 2008 0.0% 2010 80.8% 2014 

2020 pre-2007 19.2% 2008 0.0% 2010 80.8% 2015 

2021 pre-2008 10.3% 2008 0.0% 2010 89.7% 2016 

2022 pre-2009 0.0% 2008 0.0% 2010 100.0% 2016 

2023 pre-2010 0.0% 2008 0.0% 2010 100.0% 2017 

2024 pre-2010 0.0% 2008 0.0% 2010 100.0% 2018 

2025 pre-2010 0.0% 2008 0.0% 2010 100.0% 2019 

b) High Mileage Trucks in Fleets of Three or Fewer 

Under the proposed regulation, small fleets are exempt from performance requirements 

through 2011. In 2012 a small fleet must upgrade its first truck to a maximum emission 

rate equivalent to a 2004 engine with a retrofit DPF. Other vehicles in small fleets must 

be upgraded between 2013 and 2022. Using this information, we developed the 

compliance schedule identified in Table 30. 

Table 30. Compliance Assumptions for High Mileage Trucks in Fleets of Three or 

Fewer 

First truck in one-, two-, or three-truck fleet 

As of 

January 1, Model Year Turnover to 

Percent with 

DPF 

(85% Control) 

2013 pre-2003 2007 

2013 2003-2006 100% 

2018 2003-2006 2011 

2021 2007 2013 

2022 2008 2014 

2023 2009 2015 

 
G-73 

Second truck in two-truck fleet 

As of 

January 1, Model Year Turnover to 

Percent with 

DPF 

(85% Control) 

2014 pre-2003 2009 

2014 2003-2006 100% 

2016 2003-2004 2010 

2017 2005-2006 2010 

2021 2007 2013 

2022 2008 2014 

2023 2009 2015 

 

Second truck in three-truck fleet 

As of 

January 1, Model Year Turnover to 

Percent with 

DPF 

(85% Control) 

2014 pre-2003 80% to 2009 

2014 pre-2003 20% to 2010 

2014 2003-2006 100% 

2016 2003-2004 2010 

2017 2005-2006 2010 

2021 2007 2014 

2022 2008 2015 

2023 2009 2017 

 

Third truck in three-truck fleet 

As of 

January 1, Model Year Turnover to 

Percent with 

DPF 

(85% Control) 

2014 pre-2003 80% to 2009 

2014 pre-2003 

20% no 

turnover until 

2015 

2014 2003-2006 100% 

2016 2003-2004 2010 

2016 pre-2003 80% to 2009 

2016 pre-2003 20% to 2010 

2017 2005-2006 2010 

2021 2007 2015 

2022 2008 2016 

2023 2009 2017 

c) Low Mileage Trucks in Any Fleet Size 

Under the proposed regulation, low mileage trucks (defined as heavy-heavy duty diesel 

trucks driving less than 7,500 miles/yr and medium-heavy duty diesel trucks driving less 
G-74 

than 5,000 miles/yr) are allowed to delay compliance with turnover requirements until 

2020. Using this information we applied the compliance schedule shown in Table 31. 

Table 31. Compliance Assumptions for Low Mileage Trucks in Fleets of Any Size 

As of 

January 1, Model Year Turnover to 

Percent with DPF 

(85% Control) 

2012 pre-2007 20% 

2013 pre-2007 60% 

2014 pre-2007 100% 

2021 pre-2008 2010 

2022 2008 2010 

2023 2009 2010 

d) Agricultural Trucks 

Under the proposed regulation, agricultural fleets are allowed the choice of opting into 

an alternative compliance scenario. In this scenario, high mileage non-specialty trucks 

in agricultural fleets, defined as pre-1996 model year trucks driven more than 15,000 

miles per year, 1996-2005 model year trucks driven more than 20,000 miles per year, 

and 2006 and newer model year trucks driven more than 25,000 miles per year, must 

comply with regulatory provisions. Non-specialty trucks driving fewer miles are not 

required to install retrofit DPFs and are not required to meet turnover requirements until 

2016 or 2022 depending on the number of miles traveled per year. In 2016, the mileage 

threshold is reduced to 10,000 miles per year. Trucks above that threshold which had 

not previously been complying with regulatory provisions are required to upgrade to a 

2010 model year truck. Trucks below that threshold, and all specialty agricultural 

vehicles, are required to upgrade to meet 2010 model year equivalent emissions 

standards by the beginning of 2023. In Table 32 we provide our compliance 

assumptions for agricultural trucks. 

 

Table 32. Compliance Assumptions for Agricultural Trucks 

Below 10,000 miles/yr and specialty agricultural trucks 

As of 

January 1, Model Year Turnover to 

Percent with DPF 

(85% Control) 

2023 pre-2010 2012 

 

Above 10,000 miles/yr but below first mileage threshold 

As of 

January 1, Model Year Turnover to 

Percent with DPF 

(85% Control) 

2017 pre-2007 2010 

2021 2007 2012 

2022 2008 2012 

2023 2009 2012 

 
G-75 

Above first mileage threshold (15,000-25,000 miles/yr 

depending on model year) 

As of 

January 1, Model Year Turnover to 

Percent with DPF 

(85% Control) 

2011 pre-1994 100% 

2012 2003-2004 100% 

2013 2005-2006 100% 

2013 1994-1999 2008 

2014 2000-2002 2009 

2015 pre-1994 2011 

2016 2003-2004 2011 

2017 2005-2006 2011 

2021 2007 2015 

2022 1994-1999 2016 

2022 2008 2016 

2023 2000-2002 2017 

2023 2009 2017 

e) Buses 

Under the proposed regulation, non-school buses that are privately owned are assumed 

to follow BACT provisions in the proposed regulation. Due to the cost of replacing these 

vehicles we assume bus operators will achieve compliance with the least-cost option; 

this typically involves the oldest compliant vehicle available. Under the proposed 

regulation, school buses are required to install DPFs but are not required to meet NOx

emission standards. These requirements apply in addition to previous regulatory 

requirements developed under the Lower Emissions School Bus program. We provide 

bus compliance schedules in Table 33. 

Table 33. Compliance Assumptions for Buses 

School Buses 

As of 

January 1, Model Year Turnover to 

Percent 

with DPF 

(85% 

Control) 

2011 2000 and after 100% 

2012 1994-1999 100% 

2013 1987-1993 100% 

2014 pre-1987 2007 

 
G-76 

Other Buses 

As of 

January 1, Model Year Turnover to 

Percent 

with DPF 

(85% 

Control) 

2011 pre-1994 100% 

2012 2003-2004 100% 

2013 2005-2006 100% 

2013 1994-1999 2010 

2014 2000-2002 2010 

2015 pre-1994 2010 

2016 2003-2004 2010 

2017 2005-2006 2010 

2021 2007 2010 

2022 2008 2010 

2023 2009 2010 

2. Statewide Benefits 

In this section we present the statewide emissions reductions anticipated from the 

proposed regulation. 
G-77 

a) Statewide Emissions Benefits 

In Figure 36 we show the NOx emissions reduction estimated to result from the 

proposed regulation. We estimate the greatest absolute NOx emissions benefit to be 

achieved in calendar year 2014, with a reduction of 123.7 tons NOx/day relative to 

baseline emissions. In Figure 37 we show the emissions reduction estimated to result 

from the regulation, in terms of PM2.5. We anticipate the greatest emissions reduction to 

be achieved for PM2.5 in 2013, with a reduction of 13.6 tons PM2.5/day relative to 

baseline emissions. We estimate the CO2 emissions benefit resulting from the 

regulation to be negligible, with the greatest reduction of 570.8 tons CO2/day occurring 

in 2023. In some years the CO2 emissions are estimated to slightly increase due to the 

decreased fuel efficiency resulting from the technologies used to reduce NOx and PM2.5. 

We show the emissions reduction estimate for CO2 in Figure 38. 

 

Figure 36. Statewide NOx Emissions Estimates with Regulation 

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G-78 

Figure 37. Statewide PM2.5 Emissions Estimates with Regulation 

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Figure 38. Statewide CO2 Emissions Estimates with Regulation 

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G-79 

b) Statewide Emissions Benefits by Inventory Category 

In Table 34 we provide statewide NOx reductions achieved by the proposed regulation 

in calendar years 2014 and 2020 for each inventory category. As the table shows, we 

estimate that single-unit trucks will provide the greatest percent NOx reduction relative 

to their baseline, achieving 45% reductions in 2014 and 2020. (The reduction achieved 

by the proposed regulation with regard to drayage trucks is negligible for NOx and other 

pollutants in 2014 and 2020 as a result of the ARB drayage truck regulation adopted in 

2007, which will already require significant emissions reductions prior to 2014 (ARB, 

2007a; ARB, 2007b); the regulation proposed herein requires further reductions after 

2020). When considering all truck categories in aggregate, the largest share of the 

overall NOx reduction to be achieved by the proposed rule will be provided by in-state 

heavy-heavy duty tractors (42% of total reductions in 2014, 45% in 2020) and mediumheavy duty diesel trucks (20%; 17%). 

 

Table 34. California Statewide NOx Future Emissions Reductions by Inventory 

Category, 2014 and 2020 (tons/day) 

 2014 2020 

Inventory 

Category Base1 Reg2 Reduction3 Share4 Base Reg Reduction Share 

HH Out-of-State 96.3 88.2 -8.4% 6.5% 62.6 59.9 -4.3% 3.4% 

HH CA-IRP 74.0 64.1 -13.3% 8.0% 40.8 36.8 -9.9% 5.1% 

HH Tractor 140.0 88.3 -36.9% 41.7% 92.3 56.3 -39.0% 45.2% 

HH Single unit 44.9 24.5 -45.4% 16.4% 30.6 16.8 -45.2% 17.4% 

HH Drayage 33.4 33.4 0.0% 0.0% 51.4 51.4 0.0% 0.0% 

HH Agriculture 12.4 10.2 -17.6% 1.8% 7.5 4.6 -38.2% 3.6% 

HH Utility 0.6 0.6 -1.6% 0.0% 0.5 0.5 -1.3% 0.0% 

MH In-State 72.6 47.7 -34.2% 20.1% 40.2 26.3 -34.5% 17.4% 

MH Interstate 0.4 0.3 -22.2% 0.1% 0.2 0.2 -19.3% 0.1% 

MH Agriculture 2.7 2.5 -9.3% 0.2% 1.7 1.1 -38.7% 0.8% 

MH Utility 0.3 0.3 -0.8% 0.0% 0.2 0.2 -1.4% 0.0% 

Buses 11.3 9.2 -18.8% 1.7% 8.4 6.8 -19.7% 2.1% 

PTO 11.4 7.1 -37.5% 3.4% 9.2 5.4 -41.8% 4.8% 

All 500.2 376.5 -24.7% 100.0% 345.6 266.1 -23.0% 100.0% 

1

 The emissions estimated from each category, under the baseline scenario in the absence of regulation 

2

 The emissions estimated from each category, under the scenario with the proposed regulation 

3

 The percent reduction estimated from each category with the proposed regulation 

4

 The share of total emissions reductions (e.g. 123.7 tons/day, for all, in 2014) represented by the 

category 

In Table 35 we provide statewide PM2.5 reductions achieved by the proposed regulation 

in calendar years 2014 and 2020 for each inventory category. We predict that trucks in 

many categories will achieve large PM2.5 reductions but we predict the largest 

reductions to come from the single-unit category as well as the in-state medium- and 

heavy-heavy duty tractors, in terms of reductions relative to individual category baseline 

emissions. When considering all truck categories in aggregate, the largest share of the 

overall PM2.5 reduction to be achieved by proposed rule should be provided by in-state 
G-80 

heavy-heavy duty tractors (38% of total reductions in 2014; 47% of total reductions in 

2020) and medium-heavy duty diesel trucks (16%; 17%). 

Table 35. California Statewide PM2.5 Future Emissions Reductions by Inventory 

Category, 2014 and 2020 (tons/day) 

 2014 2020 

Inventory 

Category Base1 Reg2 Reduction3 Share4 Base Reg Reduction Share 

HH Out-of-State 3.7 1.7 -53.3% 15.5% 2.2 2.0 -9.0% 3.9% 

HH CA-IRP 2.9 1.1 -63.8% 14.4% 1.5 1.2 -19.8% 5.8% 

HH Tractor 6.1 1.3 -79.4% 37.7% 3.9 1.5 -61.2% 46.7% 

HH Single Unit 1.4 0.3 -79.1% 8.5% 1.0 0.3 -66.9% 13.5% 

HH Drayage 0.5 0.5 0.0% 0.0% 0.8 0.8 0.0% 0.0% 

HH Agriculture 0.5 0.3 -52.6% 2.2% 0.3 0.2 -52.4% 3.3% 

HH Utility 0.0 0.0 -4.7% 0.0% 0.0 0.0 -2.7% 0.0% 

MH In-State 2.7 0.6 -77.1% 16.3% 1.5 0.6 -60.9% 17.3% 

MH Interstate 0.0 0.0 -65.7% 0.1% 0.0 0.0 -37.2% 0.1% 

MH Agriculture 0.1 0.1 -33.4% 0.3% 0.1 0.0 -52.1% 0.8% 

MH Utility 0.0 0.0 -0.8% 0.0% 0.0 0.0 -0.8% 0.0% 

Buses 0.4 0.1 -65.2% 1.9% 0.3 0.2 -53.2% 3.4% 

PTO 0.6 0.2 -63.9% 3.1% 0.4 0.1 -74.3% 5.4% 

All 19.0 6.1 -67.7% 100.0% 12.1 6.9 -42.7% 100.0% 

1

 The emissions estimated from each category, under the baseline scenario in the absence of regulation 

2

 The emissions estimated from each category, under the scenario with the proposed regulation 

3

 The percent reduction estimated from each category with the proposed regulation 

4

 The share of total emissions reductions (e.g. 12.9 tons/day, for all, in 2014) represented by the category
G-81 

In Table 36 we provide statewide CO2 reductions achieved by the proposed regulation 

in calendar years 2014 and 2020 for each inventory category. The overall CO2

reduction for each category is negligible, with none exceeding 1% and some actually 

increasing CO2 emissions. When considering all the categories in aggregate, the 

largest share of the overall CO2 reduction in 2014 is again represented by the tractors, 

the heavy-heavy tractors from out-of-state as well as California, and the medium-heavy 

trucks from in-state. In aggregate, we anticipate a slight increase in emissions for 2014 

and a slight reduction for 2020. 

 

Table 36. California Statewide CO2 Future Emissions Reductions by Inventory 

Category, 2014 and 2020 (tons/day) 

 2014 2020 

Inventory 

Category Base1 Reg2 Reduction3 Share4 Base Reg Reduction Share 

HH Out-ofState 37,997 38,058 0.2% 23.9% 44,681 44,678 0.0% 7.1% 

HH CA-IRP 19,564 19,618 0.3% 20.8% 22,881 22,876 0.0% 12.4% 

HH Tractor 24,309 24,363 0.2% 21.2% 28,441 28,424 -0.1% 42.2% 

HH Single Unit 8,006 8,022 0.2% 6.2% 9,401 9,394 -0.1% 16.0% 

HH Drayage 8,000 8,000 0.0% 0.0% 10,393 10,393 0.0% 0.0% 

HH Agriculture 1,735 1,747 0.7% 4.5% 1,680 1,674 -0.4% 15.2% 

HH Utility 88 88 -0.2% -0.1% 98 98 -0.1% 0.2% 

MH In-State 20,993 21,051 0.3% 22.6% 22,752 22,751 0.0% 1.9% 

MH Interstate 193 194 0.2% 0.1% 210 210 0.0% 0.0% 

MH Agriculture 505 507 0.4% 0.8% 489 487 -0.4% 5.1%

MH Utility 95 95 0.0% 0.0% 105 105 0.0% 0.1% 

Buses 2,280 2,280 0.0% 0.0% 2,529 2,529 0.0% -0.1% 

PTO 1,589 1,589 0.0% 0.0% 1,878 1,878 0.0% 0.0% 

All 125,354 125,612 0.2% 100.0% 145,537 145,496 0.0% 100.0%

1

 The emissions estimated from each category, under the baseline scenario in the absence of regulation 

2

 The emissions estimated from each category, under the scenario with the proposed regulation 

3

 The percent reduction estimated from each category with the proposed regulation 

4

 The share of total emissions reductions (e.g. -258 tons/day, for all, in 2014) represented by the category
G-82 

In Table 37 we show the distribution of all emissions reductions anticipated to result 

from the proposed regulation. We expect the largest reductions in NOx and PM2.5 will 

come from trucks registered in California, with heavy-heavy instate trucks providing the 

greatest reductions followed by medium-heavy in-state trucks and single-unit trucks. 

These three categories alone represent over 74% of the overall NOx and 67% of the 

overall PM2.5 emissions reductions anticipated between 2008 and 2023. 

Table 37. Distribution of Total Future Emissions Reductions across Inventory 

Category , 2008-2023 

Inventory 

Category 

NOx

Share 

PM2.5 

Share 

HH Out-of-State 4.4% 10.8% 

HH CA-IRP 5.8% 10.3% 

HH Tractor 41.1% 40.7% 

HH Single Unit 16.0% 10.4% 

HH Drayage 6.0% 1.1% 

HH Agriculture 2.9% 3.0% 

HH Utility 0.0% 0.0% 

MH In-State 17.4% 16.4% 

MH Interstate 0.1% 0.1% 

MH Agriculture 0.6% 0.6% 

MH Utility 0.0% 0.0% 

Buses 1.7% 2.7% 

PTO 4.1% 4.1% 

Total 100.0% 100.0% 
G-83 

c) Statewide Emissions Benefits by Fleet Size 

In Table 38 we subdivide the statewide benefits for NOx reductions for 2014 and 2020 

by truck fleet size. As shown in the table, we expect the reductions in both 2014 and 

2020 to come primarily from trucks that are driven above the respective mileage 

thresholds that are included for application of the regulation. When pooling the fleets 

together, we expect that the largest share of the overall emission reduction will come 

from fleets operating more than three trucks, with the second largest share coming from 

single-truck fleets. We expect the reduction rates, however, to be largest for fleets of 3 

or fewer vehicles. In addition, we expect that nearly 45% of the NOx emissions 

reductions anticipated in 2014, and more than 47% of the reductions anticipated in 

2020, will come from fleets with three or fewer vehicles. These projections result largely 

from the fact that smaller fleets tend to have on average older trucks than do larger 

fleets. In Table 39 we show the future NOx baseline emissions and reductions as a 

function of inventory category and fleet size. 

 

Table 38. California Statewide NOx Future Emissions Reductions by Fleet Size, 

2014 and 2020 (tons/day) 

 2014 2020 

Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above threshold 134.8 98.2 -27.2% 29.6% 98.3 73.3 -25.5% 31.5% 

1 truck/below threshold 3.8 3.8 0.0% 0.0% 3.1 3.1 0.0% 0.0% 

2 truck/above threshold 38.7 26.5 -31.4% 9.8% 25.5 17.6 -31.1% 10.0% 

2 truck/below threshold 1.2 1.2 0.0% 0.0% 1.0 1.0 0.0% 0.0% 

3 truck/above threshold 23.7 17.0 -28.6% 5.5% 16.6 11.9 -28.1% 5.9% 

3 truck/below threshold 0.7 0.7 0.0% 0.0% 0.6 0.6 0.0% 0.0% 

4+ truck/above threshold 253.6 194.2 -23.4% 48.0% 169.3 136.4 -19.4% 41.3% 

4+ truck/below threshold 5.1 5.1 0.0% 0.0% 4.0 4.0 0.0% 0.0% 

Ag non specialty higher VMT 8.9 6.4 -27.5% 2.0% 4.9 2.3 -52.1% 3.2% 

Ag non specialty lower VMT 2.5 2.5 0.0% 0.0% 1.8 1.8 0.0% 0.0% 

Ag non specialty midrange VMT 2.2 2.2 0.0% 0.0% 1.6 0.6 -61.0% 1.3% 

Ag specialty vehicle 1.5 1.5 0.0% 0.0% 0.9 0.9 0.0% 0.0% 

Unspecified 23.5 17.2 -27.1% 5.2% 18.2 12.7 -30.2% 6.9% 

All 500.2 376.5 -24.7% 100.0% 345.6 266.1 -23.0% 100.0%

Table 39. California Statewide NOx Future Emissions Reductions by Inventory 

Category and Fleet Size, 2014 and 2020 (tons/day) 

 2014 2020 

Inventory Category Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above 7500 miles 5.45 4.93 -9.5% 0.4% 3.16 2.93 -7.3% 0.3% 

1 truck/below 7500 miles 0.04 0.04 0.0% 0.0% 0.03 0.03 0.0% 0.0% 

2 truck/above 7500 miles 1.69 1.52 -10.0% 0.1% 0.98 0.91 -7.2% 0.1% 

2 truck/below 7500 miles 0.01 0.01 0.0% 0.0% 0.01 0.01 0.0% 0.0% 

3 truck/above 7500 miles 1.23 1.11 -9.7% 0.1% 0.71 0.66 -7.3% 0.1% 

3 truck/below 7500 miles 0.01 0.01 0.0% 0.0% 0.01 0.01 0.0% 0.0% 

4+ truck/above 7500 miles 87.40 80.10 -8.3% 5.9% 57.36 55.01 -4.1% 3.0% 

HH Out of State 

4+ truck/below 7500 miles 0.47 0.47 0.0% 0.0% 0.37 0.37 0.0% 0.0% 
G-84 

 2014 2020 

Inventory Category Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above 7500 miles 23.01 20.90 -9.2% 1.7% 11.77 10.84 -7.9% 1.2% 

1 truck/below 7500 miles 0.16 0.16 0.0% 0.0% 0.12 0.12 0.0% 0.0% 

2 truck/above 7500 miles 8.17 7.32 -10.4% 0.7% 4.29 3.94 -8.4% 0.5% 

2 truck/below 7500 miles 0.06 0.06 0.0% 0.0% 0.04 0.04 0.0% 0.0% 

3 truck/above 7500 miles 4.53 4.03 -11.1% 0.4% 2.42 2.18 -9.9% 0.3% 

3 truck/below 7500 miles 0.04 0.04 0.0% 0.0% 0.03 0.03 0.0% 0.0% 

4+ truck/above 7500 miles 37.72 31.32 -16.9% 5.2% 21.91 19.40 -11.4% 3.2% 

HH CAIRP 

4+ truck/below 7500 miles 0.30 0.30 0.0% 0.0% 0.22 0.22 0.0% 0.0% 

1 truck/above 7500 miles 52.13 32.96 -36.8% 15.5% 33.68 19.74 -41.4% 17.5% 

1 truck/below 7500 miles 1.31 1.31 0.0% 0.0% 1.14 1.14 0.0% 0.0% 

2 truck/above 7500 miles 14.90 8.86 -40.5% 4.9% 9.90 5.67 -42.7% 5.3% 

2 truck/below 7500 miles 0.39 0.39 0.0% 0.0% 0.34 0.34 0.0% 0.0% 

3 truck/above 7500 miles 8.51 5.24 -38.4% 2.6% 5.65 3.22 -43.0% 3.1% 

3 truck/below 7500 miles 0.22 0.22 0.0% 0.0% 0.19 0.19 0.0% 0.0% 

4+ truck/above 7500 miles 61.24 38.05 -37.9% 18.7% 40.27 24.90 -38.2% 19.3% 

HH Tractor 

4+ truck/below 7500 miles 1.31 1.31 0.0% 0.0% 1.11 1.11 0.0% 0.0% 

1 truck/above 7500 miles 12.31 6.24 -49.3% 4.9% 8.54 4.06 -52.5% 5.6% 

1 truck/below 7500 miles 1.04 1.04 0.0% 0.0% 0.90 0.90 0.0% 0.0% 

2 truck/above 7500 miles 4.28 2.08 -51.4% 1.8% 3.01 1.42 -53.0% 2.0% 

2 truck/below 7500 miles 0.36 0.36 0.0% 0.0% 0.32 0.32 0.0% 0.0% 

3 truck/above 7500 miles 2.68 1.41 -47.4% 1.0% 1.80 0.87 -51.7% 1.2% 

3 truck/below 7500 miles 0.22 0.22 0.0% 0.0% 0.18 0.18 0.0% 0.0% 

4+ truck/above 7500 miles 22.37 11.56 -48.3% 8.7% 14.54 7.72 -46.9% 8.6% 

HH Single Unit 

4+ truck/below 7500 miles 1.60 1.60 0.0% 0.0% 1.31 1.31 0.0% 0.0% 

1 truck 17.94 17.94 0.0% 0.0% 27.62 27.62 0.0% 0.0%

2 truck 1.99 1.99 0.0% 0.0% 3.07 3.07 0.0% 0.0% 

3 truck 2.24 2.24 0.0% 0.0% 3.45 3.45 0.0% 0.0% HH Drayage 

4+ truck 11.21 11.21 0.0% 0.0% 17.26 17.26 0.0% 0.0% 

Ag non specialty higher VMT 7.79 5.60 -28.1% 1.8% 4.27 2.06 -51.7% 2.8% 

Ag non specialty lower VMT 1.92 1.92 0.0% 0.0% 1.38 1.38 0.0% 0.0% 

Ag non specialty midrange VMT 1.48 1.48 0.0% 0.0% 1.11 0.45 -59.3% 0.8% HH Agriculture 

Ag specialty vehicle 1.24 1.24 0.0% 0.0% 0.75 0.75 0.0% 0.0% 

HH Utility 0.60 0.59 -1.6% 0.0% 0.46 0.45 -1.3% 0.0% 

1 truck/above 5000 miles 23.96 15.18 -36.6% 7.1% 13.48 8.05 -40.3% 6.8% 

1 truck/below 5000 miles 1.20 1.20 0.0% 0.0% 0.87 0.87 0.0% 0.0% 

2 truck/above 5000 miles 7.59 4.71 -37.9% 2.3% 4.22 2.55 -39.7% 2.1% 

2 truck/below 5000 miles 0.37 0.37 0.0% 0.0% 0.26 0.26 0.0% 0.0% 

3 truck/above 5000 miles 4.52 2.90 -35.9% 1.3% 2.50 1.50 -40.0% 1.3% 

3 truck/below 5000 miles 0.22 0.22 0.0% 0.0% 0.16 0.16 0.0% 0.0% 

4+ truck/above 5000 miles 33.36 21.77 -34.7% 9.4% 17.76 12.01 -32.4% 7.2% 

MH instate 

4+ truck/below 5000 miles 1.39 1.39 0.0% 0.0% 0.95 0.95 0.0% 0.0% 

1 truck/above 5000 miles 0.05 0.05 -11.2% 0.0% 0.03 0.02 -14.4% 0.0% 

1 truck/below 5000 miles 0.00 0.00 0.0% 0.0% 0.00 0.00 0.0% 0.0% 

2 truck/above 5000 miles 0.03 0.03 -17.6% 0.0% 0.02 0.01 -21.2% 0.0% 

2 truck/below 5000 miles 0.00 0.00 0.0% 0.0% 0.00 0.00 0.0% 0.0% 

3 truck/above 5000 miles 0.03 0.03 -21.8% 0.0% 0.02 0.01 -23.0% 0.0% 

3 truck/below 5000 miles 0.00 0.00 0.0% 0.0% 0.00 0.00 0.0% 0.0% 

4+ truck/above 5000 miles 0.31 0.23 -25.5% 0.1% 0.16 0.13 -20.6% 0.0% 

MH Interstate 

4+ truck/below 5000 miles 0.01 0.01 0.0% 0.0% 0.01 0.01 0.0% 0.0% 

Ag non specialty higher VMT 1.08 0.83 -23.3% 0.2% 0.59 0.26 -55.5% 0.4% 

Ag non specialty lower VMT 0.60 0.60 0.0% 0.0% 0.44 0.44 0.0% 0.0% 

Ag non specialty midrange VMT 0.76 0.76 0.0% 0.0% 0.54 0.19 -64.4% 0.4% MH Agriculture 

Ag specialty vehicle 0.27 0.27 0.0% 0.0% 0.17 0.17 0.0% 0.0% 

MH Utility 0.32 0.32 -0.8% 0.0% 0.16 0.15 -1.4% 0.0% 

Buses 11.27 9.15 -18.8% 1.7% 8.41 6.75 -19.7% 2.1% 

PTO 11.35 7.10 -37.5% 3.4% 9.22 5.37 -41.8% 4.8% 

All 500.23 376.48 -24.7% 100.0% 345.63 266.13 -23.0% 100.0% 

In Table 40 we provide statewide PM2.5 reductions in 2014 and 2020 by truck fleet size 

and mileage threshold. We expect the reductions in both 2014 and 2020 to come from 

trucks of all different fleet sizes, regardless of whether the trucks are driven above the 
G-85 

respective thresholds included for application of the regulation. The only trucks not 

affected are agricultural trucks that are less-utilized. When considering all fleets in 

aggregate, the largest share of the overall reduction is again represented first by fleets 

operating more than three trucks, with the second largest share represented by singletruck fleets. The reduction rates, again, are greatest for fleets of 3 or fewer vehicles. 

We again expect that nearly 45% of the PM2.5 emissions reductions anticipated in 2014, 

and more than 47% of the reductions anticipated in 2020, will come from fleets with 

three or fewer vehicles. These projections again result largely from the fact that smaller 

fleets tend to have older vehicles than do larger fleets. In Table 41 we break down the 

future PM2.5 baseline emissions and reductions further as a function of inventory 

category and fleet size. 

 

Table 40. California Statewide PM2.5 Future Emissions Reductions by Fleet Size, 

2014 and 2020 

 2014 2020 

Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above threshold 5.0 1.3 -74.4% 29.2% 3.2 1.6 -48.7% 30.3% 

1 truck/below threshold 0.1 0.0 -84.0% 1.0% 0.1 0.0 -79.1% 1.7% 

2 truck/above threshold 1.5 0.4 -76.0% 8.9% 0.9 0.4 -52.6% 9.5% 

2 truck/below threshold 0.0 0.0 -83.9% 0.3% 0.0 0.0 -79.2% 0.5% 

3 truck/above threshold 0.9 0.3 -71.9% 5.0% 0.6 0.3 -49.9% 5.5% 

3 truck/below threshold 0.0 0.0 -83.9% 0.2% 0.0 0.0 -78.9% 0.3% 

4+ truck/above threshold 9.4 3.5 -63.5% 46.7% 6.0 4.0 -32.2% 37.2% 

4+ truck/below threshold 0.2 0.0 -82.8% 1.2% 0.1 0.0 -75.6% 2.0% 

Ag non specialty higher VMT 0.4 0.1 -81.2% 2.5% 0.2 0.1 -72.7% 3.0% 

Ag non specialty lower VMT 0.1 0.1 0.0% 0.0% 0.1 0.1 0.0% 0.0% 

Ag non specialty midrange VMT 0.1 0.1 0.0% 0.0% 0.1 0.0 -79.8% 1.0% 

Ag specialty vehicle 0.1 0.1 0.0% 0.0% 0.0 0.0 0.0% 0.0% 

Unspecified 1.0 0.4 -64.1% 5.0% 0.7 0.3 -64.0% 8.8% 

All 19.0 6.1 -67.7% 100.0% 12.1 6.9 -42.7% 100.0%

Table 41. California Statewide PM2.5 Future Emissions Reductions by Inventory 

Category and Fleet Size, 2014 and 2020 (tons/day) 

 2014 2020 

Inventory Category Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above 7500 miles 0.21 0.08 -60.4% 1.0% 0.11 0.10 -15.7% 0.3% 

1 truck/below 7500 miles 0.00 0.00 -81.5% 0.0% 0.00 0.00 -65.3% 0.0% 

2 truck/above 7500 miles 0.07 0.03 -60.6% 0.3% 0.04 0.03 -15.8% 0.1% 

2 truck/below 7500 miles 0.00 0.00 -81.5% 0.0% 0.00 0.00 -65.4% 0.0% 

3 truck/above 7500 miles 0.05 0.02 -60.0% 0.2% 0.03 0.02 -15.8% 0.1% 

3 truck/below 7500 miles 0.00 0.00 -81.5% 0.0% 0.00 0.00 -65.4% 0.0% 

4+ truck/above 7500 miles 3.39 1.61 -52.4% 13.8% 2.04 1.87 -8.2% 3.2% 

HH Out of State 

4+ truck/below 7500 miles 0.02 0.00 -80.1% 0.1% 0.01 0.00 -55.4% 0.1% 

1 truck/above 7500 miles 0.93 0.30 -67.8% 4.9% 0.42 0.35 -16.5% 1.4% 

1 truck/below 7500 miles 0.01 0.00 -81.9% 0.0% 0.00 0.00 -63.4% 0.0% 

2 truck/above 7500 miles 0.32 0.11 -66.4% 1.7% 0.16 0.13 -17.5% 0.5% 

2 truck/below 7500 miles 0.00 0.00 -81.8% 0.0% 0.00 0.00 -65.0% 0.0% 

3 truck/above 7500 miles 0.18 0.06 -66.0% 0.9% 0.09 0.07 -20.5% 0.4% 

3 truck/below 7500 miles 0.00 0.00 -82.3% 0.0% 0.00 0.00 -68.1% 0.0% 

4+ truck/above 7500 miles 1.44 0.58 -60.1% 6.8% 0.82 0.65 -21.0% 3.3% 

HH CAIRP 

4+ truck/below 7500 miles 0.01 0.00 -82.0% 0.1% 0.01 0.00 -70.0% 0.1% 
G-86 

 2014 2020 

Inventory Category Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above 7500 miles 2.36 0.40 -82.8% 15.2% 1.45 0.51 -65.1% 18.3% 

1 truck/below 7500 miles 0.06 0.01 -84.3% 0.4% 0.05 0.01 -79.5% 0.7% 

2 truck/above 7500 miles 0.67 0.11 -82.9% 4.3% 0.43 0.15 -65.9% 5.5% 

2 truck/below 7500 miles 0.02 0.00 -84.3% 0.1% 0.01 0.00 -80.0% 0.2% 

3 truck/above 7500 miles 0.38 0.08 -79.2% 2.3% 0.25 0.09 -65.3% 3.1% 

3 truck/below 7500 miles 0.01 0.00 -84.3% 0.1% 0.01 0.00 -80.0% 0.1% 

4+ truck/above 7500 miles 2.54 0.63 -75.1% 14.9% 1.70 0.76 -55.0% 18.1% 

HH Tractor 

4+ truck/below 7500 miles 0.06 0.01 -83.5% 0.4% 0.04 0.01 -78.0% 0.7% 

1 truck/above 7500 miles 0.38 0.07 -82.5% 2.5% 0.29 0.08 -71.5% 4.1% 

1 truck/below 7500 miles 0.03 0.00 -84.5% 0.2% 0.03 0.01 -81.0% 0.4% 

2 truck/above 7500 miles 0.13 0.02 -82.3% 0.9% 0.10 0.03 -71.2% 1.4% 

2 truck/below 7500 miles 0.01 0.00 -84.5% 0.1% 0.01 0.00 -81.0% 0.2% 

3 truck/above 7500 miles 0.08 0.02 -77.2% 0.5% 0.06 0.02 -68.8% 0.8% 

3 truck/below 7500 miles 0.01 0.00 -84.3% 0.0% 0.01 0.00 -80.4% 0.1% 

4+ truck/above 7500 miles 0.68 0.16 -76.0% 4.0% 0.50 0.20 -60.9% 5.9% 

HH Single Unit 

4+ truck/below 7500 miles 0.05 0.01 -83.6% 0.3% 0.04 0.01 -78.7% 0.6% 

1 truck 0.24 0.24 0.0% 0.0% 0.43 0.43 0.0% 0.0% 

2 truck 0.03 0.03 0.0% 0.0% 0.05 0.05 0.0% 0.0% 

3 truck 0.03 0.03 0.0% 0.0% 0.05 0.05 0.0% 0.0% HH Drayage 

4+ truck 0.15 0.15 0.0% 0.0% 0.27 0.27 0.0% 0.0% 

Ag non specialty higher VMT 0.35 0.06 -81.2% 2.2% 0.19 0.05 -71.8% 2.6% 

Ag non specialty lower VMT 0.08 0.08 0.0% 0.0% 0.06 0.06 0.0% 0.0% 

Ag non specialty midrange VMT 0.05 0.05 0.0% 0.0% 0.04 0.01 -78.2% 0.7% HH Agriculture 

Ag specialty vehicle 0.05 0.05 0.0% 0.0% 0.03 0.03 0.0% 0.0% 

HH Utility 0.00 0.00 -4.7% 0.0% 0.00 0.00 -2.7% 0.0% 

1 truck/above 5000 miles 0.91 0.19 -79.2% 5.6% 0.50 0.17 -65.3% 6.3% 

1 truck/below 5000 miles 0.05 0.01 -83.6% 0.3% 0.03 0.01 -79.3% 0.5% 

2 truck/above 5000 miles 0.29 0.06 -78.5% 1.7% 0.15 0.06 -63.7% 1.9% 

2 truck/below 5000 miles 0.01 0.00 -83.5% 0.1% 0.01 0.00 -78.7% 0.2% 

3 truck/above 5000 miles 0.17 0.04 -75.6% 1.0% 0.09 0.03 -63.1% 1.1% 

3 truck/below 5000 miles 0.01 0.00 -83.5% 0.1% 0.01 0.00 -78.5% 0.1% 

4+ truck/above 5000 miles 1.23 0.31 -74.9% 7.2% 0.63 0.29 -54.1% 6.7% 

MH instate 

4+ truck/below 5000 miles 0.05 0.01 -82.6% 0.3% 0.03 0.01 -76.4% 0.5% 

1 truck/above 5000 miles 0.00 0.00 -59.3% 0.0% 0.00 0.00 -25.5% 0.0% 

1 truck/below 5000 miles 0.00 0.00 -77.3% 0.0% 0.00 0.00 -64.4% 0.0% 

2 truck/above 5000 miles 0.00 0.00 -62.2% 0.0% 0.00 0.00 -35.7% 0.0% 

2 truck/below 5000 miles 0.00 0.00 -78.6% 0.0% 0.00 0.00 -70.3% 0.0% 

3 truck/above 5000 miles 0.00 0.00 -63.3% 0.0% 0.00 0.00 -38.8% 0.0% 

3 truck/below 5000 miles 0.00 0.00 -80.1% 0.0% 0.00 0.00 -71.0% 0.0% 

4+ truck/above 5000 miles 0.01 0.00 -66.6% 0.1% 0.01 0.00 -37.3% 0.0% 

MH Interstate 

4+ truck/below 5000 miles 0.00 0.00 -80.2% 0.0% 0.00 0.00 -69.9% 0.0% 

Ag non specialty higher VMT 0.05 0.01 -80.9% 0.3% 0.03 0.01 -79.0% 0.4% 

Ag non specialty lower VMT 0.03 0.03 0.0% 0.0% 0.02 0.02 0.0% 0.0% 

Ag non specialty midrange VMT 0.03 0.03 0.0% 0.0% 0.02 0.00 -82.9% 0.4% MH Agriculture 

Ag specialty vehicle 0.01 0.01 0.0% 0.0% 0.01 0.01 0.0% 0.0% 

MH Utility 0.00 0.00 -0.8% 0.0% 0.00 0.00 -0.8% 0.0% 

Buses 0.38 0.13 -65.2% 1.9% 0.33 0.15 -53.2% 3.4% 

PTO 0.62 0.22 -63.9% 3.1% 0.38 0.10 -74.3% 5.4% 

All 18.96 6.12 -67.7% 100.0% 12.08 6.93 -42.7% 100.0% 
G-87 

In Table 42 we subdivide the statewide benefits for CO2 reductions for 2014 and 2020 

by truck fleet size. We anticipate a slight increase for CO2 emissions in 2014 and a 

slight decrease for emissions in 2020. As noted earlier, these proposed regulations are 

not directed toward the reduction of CO2 emissions. When pooling the fleets together, 

the largest share of the overall reduction is represented by larger fleets and single-truck 

fleets in both 2014 and 2020. 

 

Table 42. California Statewide CO2 Future Emissions Reductions by Fleet Size, 

2014 and 2020 

 2014 2020 

Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above threshold 26,009 26,102 0.4% 36.4% 30,703 30,689 0.0% 33.0% 

1 truck/below threshold 394 400 1.6% 2.5% 433 438 1.1% -12.0% 

2 truck/above threshold 7,650 7,681 0.4% 12.1% 8,923 8,921 0.0% 5.3% 

2 truck/below threshold 125 127 1.6% 0.8% 138 140 1.1% -3.8% 

3 truck/above threshold 4,854 4,872 0.4% 6.8% 5,674 5,670 -0.1% 8.0% 

3 truck/below threshold 74 75 1.6% 0.5% 82 82 1.1% -2.2% 

4+ truck/above threshold 79,365 79,449 0.1% 32.6% 92,151 92,124 0.0% 65.6% 

4+ truck/below threshold 591 600 1.4% 3.2% 655 661 0.9% -14.3% 

Ag non specialty higher VMT 1,446 1,459 1.0% 5.3% 1,361 1,356 -0.4% 12.9% 

Ag non specialty lower VMT 256 256 0.0% 0.0% 245 245 0.0% 0.0% 

Ag non specialty midrange VMT 315 315 0.0% 0.0% 346 343 -0.9% 7.5% 

Ag specialty vehicle 224 224 0.0% 0.0% 217 217 0.0% 0.0% 

Unspecified 4,052 4,052 0.0% -0.1% 4,610 4,610 0.0% 0.2% 

All 125,354 125,612 0.2% 100.0% 145,537 145,496 0.0% 100.0% 

Table 43. California Statewide CO2 Future Emissions Reductions by Inventory 

Category and Fleet Size, 2014 and 2020 (tons/day) 

 2014 2020 

Inventory 

Category Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above 7500 miles 1,642 1,647 0.3% 1.9% 1,925 1,924 0.0% 0.6% 

1 truck/below 7500 miles 5 5 1.0% 0.0% 5 5 0.4% -0.1% 

2 truck/above 7500 miles 508 509 0.3% 0.6% 595 595 0.0% 0.2% 

2 truck/below 7500 miles 2 2 1.0% 0.0% 2 2 0.4% 0.0% 

3 truck/above 7500 miles 368 369 0.3% 0.4% 431 431 0.0% 0.1% 

3 truck/below 7500 miles 1 1 1.0% 0.0% 1 1 0.4% 0.0% 

4+ truck/above 7500 miles 35,407 35,460 0.2% 20.7% 41,647 41,645 0.0% 6.7% 

HH Out of 

State 

4+ truck/below 7500 miles 65 65 0.9% 0.2% 74 74 0.3% -0.5% 

1 truck/above 7500 miles 5,084 5,110 0.5% 9.9% 6,183 6,183 0.0% 2.3% 

1 truck/below 7500 miles 20 20 1.1% 0.1% 22 22 0.4% -0.2% 

2 truck/above 7500 miles 1,879 1,888 0.5% 3.4% 2,283 2,282 0.0% 1.1% 

2 truck/below 7500 miles 7 7 1.1% 0.0% 8 8 0.4% -0.1% 

3 truck/above 7500 miles 1,065 1,070 0.5% 1.9% 1,270 1,269 0.0% 0.6% 

3 truck/below 7500 miles 4 4 1.1% 0.0% 5 5 0.5% -0.1% 

4+ truck/above 7500 miles 11,469 11,483 0.1% 5.3% 13,073 13,069 0.0% 9.2% 

HH CAIRP 

4+ truck/below 7500 miles 35 35 1.1% 0.1% 37 38 0.5% -0.5% 
G-88 

 2014 2020 

Inventory 

Category Fleet Size Base Reg Red Share Base Reg Red Share 

1 truck/above 7500 miles 6,995 7,030 0.5% 13.6% 8,158 8,152 -0.1% 15.2% 

1 truck/below 7500 miles 121 123 1.7% 0.8% 136 137 1.2% -3.9% 

2 truck/above 7500 miles 2,093 2,103 0.5% 4.0% 2,443 2,443 0.0% 0.6% 

2 truck/below 7500 miles 35 36 1.7% 0.2% 40 40 1.2% -1.2% 

3 truck/above 7500 miles 1,232 1,238 0.4% 2.1% 1,439 1,437 -0.1% 3.3% 

3 truck/below 7500 miles 20 20 1.7% 0.1% 22 23 1.2% -0.7% 

4+ truck/above 7500 miles 13,682 13,681 0.0% -0.5% 16,056 16,042 -0.1% 32.5% 

HH Tractor 

4+ truck/below 7500 miles 129 131 1.5% 0.8% 148 149 1.0% -3.7% 

1 truck/above 7500 miles 1,799 1,806 0.4% 2.6% 2,103 2,100 -0.1% 7.1% 

1 truck/below 7500 miles 101 102 1.7% 0.7% 116 117 1.3% -3.7% 

2 truck/above 7500 miles 653 656 0.4% 1.1% 762 761 -0.1% 1.6% 

2 truck/below 7500 miles 35 36 1.7% 0.2% 41 41 1.3% -1.3% 

3 truck/above 7500 miles 435 437 0.4% 0.7% 510 509 -0.2% 2.0% 

3 truck/below 7500 miles 21 22 1.7% 0.1% 24 25 1.3% -0.8% 

4+ truck/above 7500 miles 4,793 4,792 0.0% -0.3% 5,652 5,646 -0.1% 16.3% 

HH Single 

Unit 

4+ truck/below 7500 miles 169 172 1.6% 1.0% 193 195 1.1% -5.3% 

1 truck 4,299 4,299 0.0% 0.0% 5,584 5,584 0.0% 0.0%

2 truck 478 478 0.0% 0.0% 620 620 0.0% 0.0% 

3 truck 537 537 0.0% 0.0% 698 698 0.0% 0.0% HH Drayage 

4+ truck 2,687 2,687 0.0% 0.0% 3,490 3,490 0.0% 0.0% 

Ag non specialty high VMT 1,205 1,217 1.0% 4.5% 1,141 1,136 -0.4% 10.8% 

Ag non specialty lower VMT 183 183 0.0% 0.0% 175 175 0.0% 0.0% 

Ag non specialty mid VMT 173 173 0.0% 0.0% 197 195 -0.9% 4.4% 

HH 

Agriculture 

Ag specialty vehicle 174 174 0.0% 0.0% 168 168 0.0% 0.0% 

HH Utility 88 88 -0.2% -0.1% 98 98 -0.1% 0.2% 

1 truck/above 5000 miles 6,164 6,186 0.3% 8.3% 6,721 6,718 0.0% 7.8% 

1 truck/below 5000 miles 146 149 1.6% 0.9% 154 156 1.1% -4.1% 

2 truck/above 5000 miles 2,024 2,032 0.4% 3.0% 2,203 2,202 0.0% 1.9% 

2 truck/below 5000 miles 46 46 1.6% 0.3% 48 49 1.1% -1.2% 

3 truck/above 5000 miles 1,201 1,206 0.4% 1.6% 1,310 1,309 -0.1% 1.9% 

3 truck/below 5000 miles 27 27 1.6% 0.2% 29 29 1.0% -0.7% 

4+ truck/above 5000 miles 11,193 11,211 0.2% 7.2% 12,087 12,086 0.0% 0.8% 

MH instate 

4+ truck/below 5000 miles 192 194 1.5% 1.1% 201 202 0.9% -4.3% 

1 truck/above 5000 miles 25 25 0.3% 0.0% 28 28 0.0% 0.0% 

1 truck/below 5000 miles 0 0 0.9% 0.0% 0 0 0.4% 0.0% 

2 truck/above 5000 miles 16 16 0.3% 0.0% 17 17 0.0% 0.0% 

2 truck/below 5000 miles 0 0 1.1% 0.0% 0 0 0.6% 0.0% 

3 truck/above 5000 miles 15 15 0.3% 0.0% 17 17 0.0% 0.0% 

3 truck/below 5000 miles 0 0 1.2% 0.0% 0 0 0.6% 0.0% 

4+ truck/above 5000 miles 135 135 0.2% 0.1% 145 145 0.0% 0.0% 

MH 

Interstate 

4+ truck/below 5000 miles 2 2 1.2% 0.0% 2 2 0.6% 0.0% 

Ag non specialty high VMT 240 242 0.9% 0.8% 221 220 -0.4% 2.1% 

Ag non specialty lower VMT 73 73 0.0% 0.0% 70 70 0.0% 0.0% 

Ag non specialty mid VMT 142 142 0.0% 0.0% 149 148 -0.8% 3.0% 

MH 

Agriculture 

Ag specialty vehicle 50 50 0.0% 0.0% 49 49 0.0% 0.0% 

MH Utility 95 95 0.0% 0.0% 105 105 0.0% 0.1% 

Buses 2,280 2,280 0.0% 0.0% 2,529 2,529 0.0% -0.1% 

PTO 1,589 1,589 0.0% 0.0% 1,878 1,878 0.0% 0.0% 

All 125,354 125,612 0.2% 100.0% 145,537 145,496 0.0% 100.0% 
G-89 

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G-90 

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G-91 

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G-92 

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