## Appendix G

Emissions Inventory Methodology and ResultsG-i T ABLE OF C ONTENTS

T ABLE OF C ONTENTS

................................................... ................................................... .... I L IST OF A CRONYMS

................................................... ................................................... ..... III A. I NTRODUCTION

................................................... ................................................... . 1 B. M ETHODOLOGY

................................................... ................................................... 1 C. D ATA S OURCES

................................................... ................................................... 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. B ASE Y EAR P OPULATION AND A CTIVITY BY A GE

................................................... ..... 6 1. Utility Fleets.....................................

................................................... ............. 8 2. Drayage Trucks Serving California’s Ports and Raily ards............................... 9 3. Trucks Serving the Agricultural Economic Sector .... ..................................... 14 4. Medium Heavy Duty Diesel Trucks .................... ........................................... 18 5. California Registered Heavy Heavy Duty Diesel Truck s ............................... 23 6. Out-of-State Heavy Heavy Duty Diesel Trucks ........ ..................................... 28
7. Buses ..............................................

................................................... ........... 33 8. Power Take Off Operations in California............ ........................................... 35 E. E MISSION R ATES

................................................... ............................................... 36 1. Updated MHDDT Emissions ............................ ............................................. 36 2. Revised engine market information.................. ............................................. 38 3. Carbon Dioxide (CO 2 ) Emission Rates................................... ....................... 40 4. Final emissions rates ..............................

................................................... ... 44 F. F ORECASTING THE B ASELINE I NVENTORY

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

................................................... ........... 44 2. Attrition..........................................

................................................... ............. 46 3. Pre-buy............................................

................................................... ........... 47 G. S TATEWIDE B ASELINE A CTIVITY AND E MISSIONS

................................................... ... 53 1. Base Year Age Distributions across Inventory Catego ry............................... 54 2. Base Year and Forecasted Population by Inventory Ca tegory...................... 56 3. Comparing Accrual Rates by Inventory Category ...... ................................... 57 4. Base Year and Forecasted Vehicle Miles Traveled by Inventory Category... 58G-ii

5. Statewide Baseline Emissions Estimates............. ......................................... 62 H. S TATEWIDE T RUCK AND B US R EGULATION B ENEFITS ............................................... 68 1. Methodology to Assess Statewide Benefits........... ........................................ 68 2. Statewide Benefits .................................

................................................... .... 76 I. R EFERENCES

................................................... ................................................... .. 89G-iii

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 rateG-1

A. Introduction Commercial heavy-duty diesel trucks and buses (defi ned as commercial diesel buses and trucks exceeding 14,000 lbs gross vehicle weigh t rating (GVWR)) are currently the single largest source of nitrogen oxide (NO x ) emissions in California, accounting for 30% of statewide NO x

emissions (ARB, 2008). These same trucks buses ar e 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 c urrently calculated using the ARB’s EMFAC2007 model that was released in December of 20 06 (ARB, 2006). Since the last EMFAC release, Staff members have conducted a comprehensive re-evaluation of the heavy duty diesel truck emissions inventory. I n developing this new analysis, we have integrated new data and assumptions into an ex panded methodology that builds upon current modeling in EMFAC2007. With this meth odology, we incorporate detail for different types of trucking operations and truck co nfigurations that are referred to as “inventory categories”. Emission factors differ fr om those in EMFAC2007 and reflect our enhanced knowledge of trucking operations in Ca lifornia that has been developed through this effort. With this document we describ e our approach and the results from its application. With this document we also provid e 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, vehicl e population-based technique for estimating emissions for any type of on-road ve hicle. 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 tr aveled, and emission rates per vehicle model year in a given calendar year, growth and attrition estimates, deterioration rates, and other factors that affect emissions esti mates.
Our revised approach for estimating commercial heav y-duty diesel truck emissions builds upon this concept by applying it separately for each category of trucks. These categories were selected by evaluating different gr oups of trucks that have similar travel, service, size, age or other characteristics within the category but differing between categories.G-2

The methodology used to develop the proposed rule i nventory is based on the following equation:

EMS CY = 

# ∑ MY, C 

# ( POP MY, C X AC MY, C X ER MY, C 

# )

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

CY. POP MY, C is the population of trucks for model year MY with in each inventory

category C for a given calendar year;

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

MY and inventory category C in a given calendar yea r;

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

assuming statewide speed travel distributions in EM FAC2007 and category-specific cumulative mileage accrual over t he life of the truck, by model year MY and inventory category C;

With this new analysis, we developed a population a nd model year distribution for each vehicle category. We also estimated accrual by mod el year for the category and cumulative mileage accrual (odometer) by model year . Because trucks can move between categories as they age, we assessed the mov ement of used trucks between categories in order to develop cumulative mileage a ccrual estimates that reflect this movement. As a result, cumulative odometer reading s by model year will not necessarily be consistent with accrual schedules fo r each inventory category. We developed emission rates using EMFAC2007 and statew ide speed distributions, and we adjusted emission rates for modeled odometer readin gs 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 gene ral description of each data source as well as links for further information.

1. Motor Vehicle Registration Data The California Department of Motor Vehicles (DMV) i s 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 a nnually; 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 dow nloads 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, w hich separates International Registration Plan (IRP) vehicles from non-IRP vehic les, each vehicle’s rated weight, and each vehicle’s body type data, we separated veh icles into different inventory categories. As such DMV registration was the prima ry source for vehicle populationG-3

and model year by category. We also used the DMV r egistration data to group trucks by fleet and thus fleet size. With these data we c ould 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 catego ries. 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 th is report describing each inventory category.

2. International Registration Plan (IRP) The International Registration Plan is a program ad ministered by the American Association of Motor Vehicle Administrators to tran sfer registration fees assessed to commercial and other vehicles that cross state boun daries, in accordance with the number of miles affected vehicles travel in each pa rticipating 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 go vernment vehicles and smaller vehicles. Under this program a fleet of vehicles h as the option of registering their vehicles in any state where a portion of their flee t is domiciled.

IRP recordkeeping varies by state with some states maintaining electronic databases and others paper files. To facilitate data analysi s, Staff obtained IRP data from states in electronic format whenever possible. Staff receive d in electronic format comprehensive reports representing mileage driven by California-r egistered IRP trucks in calendar years 2000-2006, and obtained hardcopy samples of I RP data from a number of other jurisdictions for miles driven by their IRP trucks in California.

IRP data representing California trucks were used t o 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 update s 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 n on-neighboring states (Alabama, Indiana, Nebraska, New Jersey, Oklahoma, Tennessee, Texas, and Wisconsin). Of these states, Alabama, Nebraska, New Jersey, and Ok lahoma each provided a model year or Vehicle Identification Number (VIN) for eac h truck in each fleet, allowing detailed age distributions for each of these states to be de veloped. Staff relied on roadside survey data to develop age distributions for other states.

All IRP data are provided at the level of a fleet r ather than the level of an individual vehicle. Each fleet registered with the IRP report s the total number of power units or trucks in the fleet, the total miles traveled by th at fleet, and the total miles traveled in California by that fleet. Reporting is completed a nnually. Many fleets reporting to IRP are large; as a result, the population of trucks re flected 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 truc k in the fleet, while other statesG-4

don’t. Where model year data were available, staff used the information directly. Where model year data were not available but VIN da ta were, staff decoded the VIN to derive model year. Staff also received a summary r eport 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 Cal ifornia Highway Patrol (CHP) jointly administer the Motor Carrier Permit Program (MCPP), which applies to any operator of a commercial vehicle in California exce eding 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 Cali fornia. 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 truc ks in each fleet.

We obtained the DMV/CHP 2005 database and used it t o estimate the fraction of truck owner-operators in California. These data were com pared to similar estimates derived from DMV registration data. Because the MCPP datab ase 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 o wners that are owner-operators.

4. International Fuel Tax Agreement (IFTA) The International Fuel Tax Agreement (IFTA) is an a greement among U.S. states and Canadian provinces to simplify the reporting of fue l use taxes by interstate motor carriers. In California, IFTA is administered by th e Motor Carrier Section of the Board of Equalization (BOE) (BOE, 2008). The program operat es similarly to IRP in that motor carriers may choose a state for filing fuel tax ret urns 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. F or 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. How ever, data for 2006 and 2007 were incomplete with about 10% of the data in each of th ose years not summarized. For states with incomplete data, we extrapolated estima tes 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 f rom neighboring and nonneighboring states.G-5

5. Vehicle Inventory and Use Survey (VIUS) The main intent of the Vehicle Inventory and Use Su rvey (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 nati onal and state-level estimates of the total number of trucks. Up until 2002, this su rvey was conducted every 5 years by the U.S. Census Bureau as part of the national cens us 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 ma ny other factors.

We used VIUS data reported by trucks operating in C alifornia 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 a n 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 truc ks, and others. These surveys provided information such as truck age, miles trave led, 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 co nducted by the Coordinating Research Council (CRC) under the E55/59 testing pro gram (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 dioxid e emission rates were updated based upon new analysis of several different data s ources. 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. Res earchers administered 433 surveys of truck drivers at seven weigh stations near state bo rders with high commercial truckG-6

traffic (Lutsey, 2008). These surveys provided es timates of annual travel activity in California for each of the respondents, allowing st aff to estimate the number of out-ofstate trucks, their mileage, fuel usage, and fuelin g locations. Based on results of this study, staff examined whether estimates of intersta te truck age, mileage, and fuel usage assumed in EMFAC2007 were reasonable.

Staff also used raw data from this analysis to vali date estimates of model year distribution for out-of-state trucks that were deve loped using IRP data. In an accompanying report, UC Davis estimated the fractio n of total VMT in California represented by out-of-state trucks relative to EMFA C2007. ARB did not use these estimates because, subsequent to the Lutsey (2008) report being finalized, staff received IFTA data from the BOE that provided direc t mileage reports by state of registration. These issues will be described in gr eater detail later in this document.

D. Base Year Population and Activity by Age In Table 1 below, we provide a list of the data sou rces underlying each of the truck inventory categories to be discussed in the next se ction including how each data source was used to help develop truck population and activ ity estimates.

Table 1. Data Sources Used to Develop Population a nd 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.G-7

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 Pu blic 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 th is inventory is that trucks and buses that are grouped by vocation or body type hav e a common age distribution and accrual schedule. This assumption has been verifie d through analysis of the data sources described above.

Another assumption that applies to many truck inven tory categories is that trucks typically move between categories as they age and a ccrue mileage. Staff recognized this through analysis of DMV data. For example, he avy-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 increasi ng maintenance costs and bought for intrastate travel where reliability is less of a co ncern. Vehicles are driven many more miles during interstate travel than during intrasta te 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 yea rs. Staff used estimated as to when vehicles would most typically be transferred betwee n inventory categories and adjusted the odometer readings from those assumed in EMFAC20 07 appropriately.

This section describes each vehicle category includ ing the key assumptions and data analysis results that underlie the development of t he 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 betweenG-8

different fleet sizes where appropriate and the dif ference in the average age of vehicles between different-size fleets.

1. Utility Fleets In October 2006, the ARB adopted a regulation desig ned 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 wei ght rating or GVWR - MHDDT) and heavy-heavy (>33,000 lbs GVWR - HHDDT) trucks b ut does not include refuse haulers or fire trucks and other emergency vehicles operated by public agencies. The regulation required fleet operators to reduce diese l 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 Uti lity Fleet emissions inventory.

a) Base Year Population and Age Distribution by Fle et Size Staff used an identical methodology for assessing t he 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 th ose names to extract vehicles from DMV registration data. Those vehicles were then se parated 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 yea rs for HHDDT, 7.2 years for MHDDT. Figure 1 provides the distribution of the Californi a 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 AgePopulation

Utility MH Utility HHG-9 b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds

Staff used accrual rates and cumulative odometer re adings 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 estimate d annual average mileage accrual was 7,800 miles per year for HHDDT and 6,20 0 miles per year for MHDDT, all of which is assumed to occur in California. The es timated average odometer reading was 122,000 miles for HHDDT and 78,000 miles for MH DDT. In Figure 2 we plot annual accrual and cumulative modeled odometer read ings for utility category trucks.

Figure 2: Utility Truck Category Accrual and Model ed 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 AgeAccrual (miles/yr)

0 100,000 200,000 300,000 400,000 500,000Odometer (miles)

Utility MH Accrual Utility HH Accrual Utility MH Odometer Utility HH Odometer

c) Base Year Vehicle Miles Traveled by Fleet Size a nd Mileage Thresholds Utility fleet vehicles were not separated into diff erent categories by fleet size or mileage threshold. This vehicle and emissions inventory ev aluates only the fleets of privately owned utilities. The utility fleets of larger muni cipal agencies are not included in this assessment.
2. Drayage Trucks Serving California’s Ports and Ra ilyards In 2007 the ARB passed a regulation requiring NO x 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 Cal ifornia and primarily serve the Ports of Los Angeles, Long Beach and Oakland along with rail yards near those ports. DrayageG-10

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 regula tion, by calendar year 2010 all drayage trucks must meet at a minimum model year 19 94 or later emissions standards with a diesel particulate filter and by 2014 all tr ucks must meet 2007 truck emission standards. The following methodology was used to d evelop the drayage truck emissions inventory:

a) Base Year Population and Age Distribution by Fle et Size Drayage trucks are defined by their operation rathe r than body type. Since DMV registration records provide information on body ty pe but not vehicle operator, DMV registration cannot be used alone to estimate the s tatewide drayage truck population. The population of trucks serving the Ports of Los A ngeles and Long Beach and associated railyards was derived from an analysis o f one year of gate count and license plate information from approximately half of the te rminals at the two Ports; these data were provided by officials from the ports. Staff c ompared 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 us ed for the Port of Oakland and associated railyards. Regulatory documentation (AR B, 2007b) describes in more detail the methodology used to assess the population of dr ayage trucks. We estimated the age of drayage trucks serving other ports and raily ards in California by assuming that these trucks were similar to those in the HHDDT (in state) tractor category (ARB, 2007b). The population-weighted average age of a v ehicle in the drayage category was estimated at 12.4 years for trucks serving the Port s of Los Angeles and Long Beach and regional intermodal railyards and 9.7 years for dra yage trucks serving Oakland and associated railyards and for trucks serving the rem ainder of California ports. In Figure 3 we provide drayage truck population by model year f or calendar year 2008. As shown, trucks serving the Ports of Los Angeles and Long Be ach are typically several years older than drayage trucks serving other California ports.G-11

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 AgePopulation

LA/LB Region Bay Area Other portsG-12 b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds

Staff used accrual rates and cumulative odometer re adings published in the drayage truck rule staff report (ARB, 2007b) to estimate th e vehicle miles traveled (VMT) associated with drayage trucks in each region. Sta ff 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 cat egory.
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 characteristi cs similar to in-state HHDDT tractors and therefore drive, on average, 49,000 miles per y ear. All drayage-related VMT are assumed to accrue in California. Odometer readings are modeled assuming that drayage trucks were purchased used from larger nati onal 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 categor y. The resulting average odometer reading as estimated was 860,000 miles for LA/LB tr ucks and 770,000 miles for trucks serving Oakland and other ports/railyards; the appa rent 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 d rayage trucks. In Figure 4 we plot annual accrual and cumulative modeled odometer read ings for drayage trucks.

Figure 4. Drayage Truck Category Accrual and Model ed Odometer Readings (2008)

0 20,000 40,000 60,000 80,000 100,000

-1 1 3 5 7 9 1113151719212325272931333537394143 AgeAccrual (miles/yr)

0 400,000 800,000 1,200,000 1,600,000 2,000,000Odometer (miles)

LA/LB Accrual Oakland Accrual Other Ports Accrual

LA/LB Odometer Oakland Odometer Other Ports OdometerG-13 c) Base Year Vehicle Miles Traveled by Fleet Size a nd Mileage Thresholds Drayage trucks were categorized by fleet size as si ngle truck fleets, two truck fleets, three truck fleets, and fleets of more than three t rucks. 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. I n Table 2, Table 3, and Table 4 we show the distribution of VMT among drayage fleets o f various fleet sizes and VMTweighted ages for trucks near the Ports of Los Ange les/Long Beach, Oakland, and all other California ports/railyards, respectively. Th e age distribution of drayage trucks was also assumed not to differ with regard to fleet siz e. With regard to the VMT-weighted average age within each category, drayage trucks ne ar LA/LB were estimated to be older, on average, than drayage trucks near other f acilities. 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 wit h staff, administered a survey to farmers, ranchers, and other agricultural businesse s designed to identify and characterize trucks associated with agricultural bu sinesses. The stakeholder survey was designed primarily to capture trucks owned and operated by farms, ranches, and first processing facilities, and was similar in for mat to the on-line survey used to collect information on the broader truck fleet as part of t his rule development (ARB, 2008). Results from the survey were used to assess the pop ulation of MHDDT and HHDDT that operate in California for agricultural purpose s, either seasonally or annually. The survey was also used to assess the model year distr ibution 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 Fle et Size To assess the model year distribution of agricultur al trucks, we compiled survey results for MHDDT and HHDDT separately, and modeled the dat a in order to smooth trends in model year variability. Based on analysis of the s urvey data, the population-weighted average ages of HHDD and MHHD agricultural trucks w ere found to be very similar (17.3 years for HHDDTs and 17.2 years for MHDDTs).

To assess population, we compiled the survey result s 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 specifi c truck populations estimated using the extrapolated data to model year specific DMV registration data, they found that the extrapolated agricultural truck population s for certain model years exceeded the total number of trucks of that model year in the DM V database. This suggested that the directly extrapolated survey results were overestim ating 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 p opulation using the metrics described above would overestimate the agricultural truck pop ulation.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-calcu lated an agricultural truck population assuming that the agricultural truck pop ulation, in any model year, could not exceed 80% of the total trucks registered in that m odel 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 Ca lifornia agricultural truck population and model year distribution estimated fo r 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 AgePopulation

Ag Trucks MH Ag Trucks HH

b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds We analyzed survey results in order to estimate mil eage accrual for agricultural trucks. Because agricultural HHDDT are assumed to be purcha sed 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 likelih ood that most tractors currently in the agricultural category were previously in other cate gories. Because in-state tractors and single-unit trucks are driven more, on average, tha n 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 t rucks then follow the agricultural truck accrual rate upon transition. Agricultural M HDDT also followed a composite rate, since in-state MHDDT were also assumed to migrate t oward the agricultural category. The average odometer reading for agricultural was e stimated to be approximatelyG-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 Accr ual and Modeled Odometer (2008)

0 10,000 20,000 30,000 40,000 50,000 60,000

-1 1 3 5 7 9 1113151719212325272931333537394143 AgeAccrual (miles/yr)

0 200,000 400,000 600,000 800,000 1,000,000 1,200,000Odometer (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 Mi leage Thresholds Staff did not categorize agricultural trucks by fle et size. With regard to application, both medium-heavy and heavy-heavy trucks were categorize d into specialty and nonspecialty vehicles. Specialty vehicles are specif ically 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 feed lot. Using this definition, by analyzing the DMV registration data, we found that 10% of agr icultural trucks or approximately 2200 agricultural trucks statewide were specialty v ehicles.G-17

Non-specialty agricultural trucks were categorized according to mileage thresholds, since the regulation is to be applied differently t o 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 ex ceed 10,000 miles per year but do not exceed an upper mileage threshold need to turno ver or retrofit their trucks prior to 2017. Agricultural trucks are below the upper mile age threshold if they fall into one of the following three categories:

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

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

 MY 2006 or newer, and driven less than 25,000 mile s/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 t he 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 am ong medium-heavy agricultural trucks in California in calendar year 2008. In Tabl e 7 we show the distribution of daily VMT among heavy-heavy agricultural trucks in Califo rnia in 2008. In each table we also show the average VMT-weighted age by mileage thresh old. 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 Ag ricultural Trucks by Fleet Size (2008)

Vehicle Type Daily VMT Share VMT-weighted Average Age Non specialty, below lower threshold 46,021 13.4% 1 6.0 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 Agr icultural 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 reg istered in and operate in California. These trucks are primarily lighter wei ght delivery trucks with a GVWR between 14,000 and 33,000 lbs that travel during th e work day within the area where they are registered. Most of these trucks are regi stered 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 foun d in fleets of two or more although some are single-truck-fleet.G-19 a) Base Year Population and Age Distribution by Fle et Size Staff identified MHDDT in the DMV registration data base 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 dat a by owner name and address to identify the number of vehicle records associated with a uni que owner. This list was then used to develop the fleet size distribution, including t he number of owner-operators and small fleets. Staff then used a combination of license p late and IRP registration data to estimate how many of the MHDDT are registered in Ca lifornia 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-stat e trucks fall into the MHDDT category. Since both the public and agricultural truck catego ries include MHDDT, we subtracted these by model year from the in-state MHDDT categor y 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 MHDDT s operating in California.

Figure 7. MHDDT Population and Model Year Distribu tion (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 AgePopulation

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 M HDDT. 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 mile age accrual that occurs in California on average. Staff estimated that CA IRP MHDDT drive 6 3% and out-of-state registeredG-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 a nd 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 pl ot 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 AgeAccrual (miles/yr)

0 400,000 800,000 1,200,000 1,600,000 2,000,000 2,400,000Odometer (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 OdometerG-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-regi stered trucks. However, these accrual rates are simply the total accrual rates fo r the vehicles in each inventory category; they do not represent the accrual rate fo r vehicles in each category within California. Because the share of annual travel wit hin California varies between categories (8% for Out-of-State; 63% for CA-IRP; 10 0% for MHDDT Instate), the accrual rate for vehicles within California also va ries for each category. We show these accrual rates in Figure 9; because California-regis tered MHDDT are assumed to spend 100% of their time in California, they have the hig hest 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 AgeAccrual (miles/yr)

0 200,000 400,000 600,000 800,000 1,000,000 1,200,000Odometer (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 a nd Mileage Thresholds We categorized medium-heavy duty diesel trucks by f leet 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 drive n since the regulation is to be applied differently to vehicles used at differing m ileage levels. The mileage threshold of significance for MHDDT from a regulatory perspectiv e is 5,000 miles per year. In Table 8, Table 9, and Table 10 we show the distribution o f daily VMT driven in California in calendar year 2008 by different size fleets of inst ate, CA-IRP, and out-of-state MHDDTs, respectively. Each table also shows the VM T-weighted average age of the trucks as a function of fleet size. For all catego ries of MHDDT, regardless of miles driven or state of registration, the smaller the fl eet, the older the truck. Also, very littleG-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 t hose in comparable size fleets that drive more than 5,000 miles a year.

Table 8. Distribution of VMT among Medium-Heavy In state 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 CA IRP 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 O ut-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 Tr ucks 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 local ized 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 si gnificant 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 p er 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 truc ks 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 Fle et Size To develop population estimates for in-state HHDDT, staff used DMV registration data (license plate number and GVWR) to identify those H HDDT 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 da ta to assess the body type of each in-state truck. We then used the DMV data to estim ate the population and model year distribution of in-state single-unit trucks, in-sta te tractors, and California IRP trucks. We subtracted the populations of utility trucks, draya ge trucks, and agricultural trucks estimated elsewhere from the in-state HHDDT categor y, by body type and registration, to estimate the number of in-state tractors and avo id double counting.G-24

In Figure 10 we provide the estimated population an d age distribution for in-state HHDDT categories in California in calendar year 200 8. In general, vehicles traveling only in California are older than those traveling o ut-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 avera ge 10.2 years old.

Figure 10. California-Registered In-State HHDDT Po pulation 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 AgePopulation

HH Instate HH Single Unit HH CAIRP

b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds Staff used VIUS to assess annual mileage accrual ra tes by body type for in-state HHDDT and CA-IRP trucks. This analysis indicated t hat California IRP trucks drive about 55% of their total miles in California. Beca use 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 earl y years, we developed a composite modeled odometer to represent in-state tractors. T he 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 beg ins shorter haul service in the instate category. For comparative purposes, a vehicl e 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 e xclusively 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 betw een 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 mi les for CAIRP HHDDT. TheG-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 mile s 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 d ata reflect the assumption of composite use. For comparative purposes, in Figure 12 we provide the composite odometer reading for instate trucks of various mode l years in calendar year 2008 compared with the modeled odometer reading for simi lar trucks used purely for in-state or interstate long-haul traffic.

Figure 11. HHDDT In-state and Interstate Fleet Acc rual and Modeled Odometer (2008)

0 20,000 40,000 60,000 80,000 100,000 120,000

-1 1 3 5 7 9 1113151719212325272931333537394143 AgeAccrual (miles/yr)

0 400,000 800,000 1,200,000 1,600,000 2,000,000 2,400,000Odometer (miles)

HH CA Instate Accrual HH Single Unit Accrual HH CAIRP Accrual HH CA Instate Odometer HH CA Single Unit Odometer HH CAIRP OdometerG-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 AgeOdometer (miles)

Pure Instate Tractor Adjusted Instate Tractor Pure Interstate TractorG-27 c) Base Year Vehicle Miles Traveled by Fleet Size a nd Mileage Thresholds We categorized heavy-heavy duty diesel trucks by fl eet size as single-truck fleets, twotruck fleets, three-truck fleets, and fleets of mor e than three trucks. We also categorized the fleets by their annual VMT since th e regulation is to be applied differently to trucks driven at different mileages each year. The mileage threshold of significance for HHHDT from a regulatory perspectiv e is 7,500 miles per year. In Table 11 we show the distribution of daily VMT and the VM T-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 dr iving less than 7,500 miles a year are significantly older (5 years) than their counterpar ts 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 Californ ia 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 a s 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 a s a function of fleet size and mileage driven in California for heavy-heavy trucks license d in California under the International Registration Program in California in 2008. Each t able also shows the average VMTweighted age of vehicles within the fleets of diffe rent sizes; in each case, the trucks driven fewer miles tended to be older than trucks d riven more miles, and the average age of vehicles within a fleet was also estimated t o decrease with the size of the fleet.

Table 12. Distribution of Daily VMT among Single-U nit 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 Californ ia 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, c onstruction, and transportation are all major contributors to California’s economy, and the se economic sectors attract the services of national trucking fleets including larg e numbers of out-of-state heavy-heavy duty diesel trucks.G-29 a) Base Year Population and Age Distribution by Fle et Size No single source of information describes out-of-st ate truck activity in California. To develop an inventory, staff conducted detailed rese arch 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 sugg ested that more than one million trucks are in fleets that report mileage in Califor nia. However, this is likely to be an upper estimate as not all of the trucks in these fl eets actually enter California. To better constrain the number of out-of-state trucks enterin g California, staff analyzed IRP data in greater detail, as described below.

In analyzing the age distribution of out-of-state t rucks, 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. Nei ghboring states were considered to include British Columbia, Washington, Oregon, Idaho , Nevada, and Arizona while nonneighboring states included all other states and Ca nadian provinces. Staff sampled IRP data from 12 states to obtain a statistically repre sentative 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 intersta te trucks while non-neighboring state trucks are on average around 3.3 years old, much yo unger than other interstate trucks. Recent field studies supported by the ARB have conf irmed 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 colle cted 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 leng th 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 mil es traveled in California divided by the assumed trip length. For all other fleets we assum ed all of the reported trucks in that fleet actually enter California. Staff then recomp iled 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 stat es 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 fr om non-neighboring states were estimated to be on average 3.6 years old. These es timates were each close to the IRP estimates. In Figure 13 we provide estimates of t he 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 AgePopulation

HH NOOS HH NNOOSG-31 b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds

Staff used VIUS to assess accrual rates for the nat ionwide IRP truck category traveling in California. Staff then evaluated IRP sample dat a to estimate the fraction of miles accrued in California and 2005 IFTA records to quan tify the total miles traveled in California by out-of-state HHDDT. Results suggeste d 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 mile s per year (30,000 miles per year in CA) for neighboring out-of-state HHDDT and 85,00 0 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 o dometer by model year for out-ofstate heavy-heavy duty diesel trucks operating in C alifornia 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 AgeAccrual (miles/yr)

0 400,000 800,000 1,200,000 1,600,000 2,000,000 2,400,000Odometer (miles)

HH NOOS Total Accrual HH NNOOS Total Accrual
HH NOOS Odometer HH NNOOS OdometerG-32

In Figure 14, the accrual rates for HHDDT NOOS and NNOOS trucks are shown to be equal, but these rates represent the overall accrua l for the trucks. Because trucks from states neighboring California drive a larger fracti on 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 catego ry in California. We show these in Figure 15.

Figure 15. Out-of-State Truck Accrual within Calif ornia 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 AgeAccrual (miles/yr)

0 400,000 800,000 1,200,000 1,600,000 2,000,000 2,400,000Odometer (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 a nd Mileage Thresholds Staff also categorized heavy-heavy duty diesel truc ks from outside California by fleet size as single truck fleets, two truck fleets, thre e 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 u sed 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 a verage age to be higher for vehiclesG-33

driven fewer miles. We estimated the average age t o be independent of the fleet size for vehicles registered outside California.

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

Trucks Registered in Neighboring States by Fleet Si ze (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-He avy 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 reductio ns for two classes of buses: school buses and other buses. School buses may be either privately- or publiclyowned, but must be used exclusively for transportin g students in accordance with the definition of school buses in the California Vehicl e 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 cate gory includes intercity buses, charter buses, and church buses. This section describes th e assumptions used to develop a baseline emissions inventory for school buses and o ther buses.

a) Base Year Population and Age Distribution by Fle et Size Staff used a calendar year 2005 database of school buses compiled by the CHP to estimate the population and age distribution of sch ool buses in California in 2005 (California Highway Patrol, 2006). The age distrib ution of school buses in 2005 wasG-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 EMF AC2007 and the existing Lower Emissions School Bus Program (LESBP) to estimate th e 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 replac ed 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 tha t 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 a ssumed 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 aggreg ate population of diesel school buses was assumed to remain unchanged; any increase s to the total school bus population were assumed to represent new vehicles u sing natural gas or some other alternative fuel entering the fleet.

Other buses were assumed to follow the age distribu tion 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 com pare the age distribution of school buses and other buses operating in California as mo deled for calendar year 2008 in Figure 16.

Figure 16. Bus Population and Model Year Distribut ion (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 AgePopulation

School Bus Other BusesG-35 b) Base Year Accrual, Lifetime Accrual, and Mileage Thresholds
In the absence of new information, staff used the a verage annual mileage accrual and odometer estimates assumed for buses in EMFAC2007 t o estimate the benefits of the proposed rule. The average odometer reading was est imated to be 173,000 miles for school buses and 222,000 miles for other buses. Th e 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 est imates 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 AgeAccrual (miles/yr)

0 100,000 200,000 300,000 400,000 500,000 600,000Odometer (miles)

School Bus Accrual Other Buses Accrual
School Bus Odometer Other Buses Odometer

c) Base Year Vehicle Miles Traveled by Fleet Size a nd Mileage Thresholds Buses were not differentiated by fleet size or mile age threshold in this inventory as buses are not treated differently in the proposed r egulation based on these criteria.

8. Power Take Off Operations in California Power Take Off (PTO) operations are those that resu lt 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 operatio ns 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 dis tribution of PTO activity. Instead we used fuel consumption data for PTO operations in Ca lifornia 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 f ollow 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 equ ipment 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 Ca lifornia, 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 c ase, a reference is provided for more detailed information as to the basis for the e mission rates. The specific emission rates assumed for vehicles of each fleet will be pr ovided 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 fac tors for medium heavy heavy-duty trucks (MHDDT). Subsequent to the release of EMFAC 2007, the CRC made emissions test data available for MHDDT available to staff th rough 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 analy sis, staff merged the E55/59 data into the data set that was used to develop MHDDT em ission factors for EMFAC2002. From this combined data set, staff then recalculate d revised zero-mile rates (ZMR) and deterioration rates (DR) for MHDDT.

The method for deriving MHDDT ZMR and DR is the sam e 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 d eterioration rates for medium-heavy duty diesel trucks assumed in EMFAC2007. The deter ioration rates are used to model the increase in emissions relative to the zero-mile (or new-vehicle) emission rate due toG-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)

HC CO NO x PM 10 MY 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 0.0 16
1975-76 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.01 6
1977-79 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.01 6
1980-83 0.34 0.011 3.17 0.100 18.50 0.032 1.07 0.01 6
1984-86 0.33 0.014 2.99 0.131 17.91 0.043 1.00 0.02 1
1987-90 0.21 0.016 1.80 0.140 15.74 0.034 0.73 0.01 7
1991-93 0.18 0.018 1.43 0.139 13.11 0.078 0.45 0.02 2
1994-97 0.11 0.017 0.78 0.121 11.55 0.048 0.27 0.01 8
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 0.01 2
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 0.0 09

Table 17. Revised MHDDT Zero-Mile Emission Rate (Z MR, g/mi) and Deterioration Rate (DR, g/mi/10,000 mi)

HC CO NO x PM 10 MY 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 0.0 38
1987-90 0.65 0.056 2.19 0.189 15.39 0.044 1.05 0.03 4
1991-93 0.29 0.025 1.12 0.095 11.51 0.053 0.57 0.02 6
1994-97 0.21 0.028 0.83 0.109 11.30 0.068 0.31 0.01 7
1998-02 0.22 0.028 0.84 0.108 11.11 0.078 0.35 0.01 5
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 0.001G-38

2. Revised engine market information Staff also updated the emission factors for heavy h eavy-duty diesel trucks (HHDDT) and medium heavy-duty diesel trucks (MHDDT) to refl ect manufacturers’ compliance with the 2007 engine standard and the anticipated c ompliance with the 2010 engine standard between 2006 and 2011. Staff estimated th at the adjustment to 2006-2011 model year emission factors would result in lower N O x emission rates for 2006 and 2009 model years but higher NO x emission rates for 2007, 2008, 2010, and 2011 mode l 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 t echnology in their 2010-2011 model year heavy-duty diesel engines. In addition, revie w of the certification data shows that some engine manufacturers introduced 1.2 g/bhp-hr N O x 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 ma nufacturers and certification data. For the earlier introduction of engines meeting 1.2 g/bhp-hr NO x and 0.01 g/bhp-hr PM standards, the current zero-mile rates (ZMR) and de terioration 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 mode l year group (1.2 g/bhp-hr NO x engines) but assumed that the deterioration rate fo r 2006-09 model year would still apply to the 0.5 g/bhp-hr NO x engines. The assumption of unchanged DR is largel y based on the fact that the engine manufacturers wil l achieve a 0.5 g/bhp-hr NO x level with an integrated technology solution based on the ir 2006-09 model year technologies (advanced fuel system, air management, combustion a nd electronic controls, and enhanced cooled exhaust gas recirculation).G-39

Staff estimated the 2005-2008 model year sales frac tions of heavy and medium heavyduty diesel engines based on the sales fraction dat a reported by the manufacturers and projected the 2009-2012 model year sales fractions from the information currently available. We show the ARB-estimated penetration r ates for 2005 to 2012 model years in Table 18.
Table 18. ARB Estimated Penetration Rates of 2005- 2012 Model Year Engines

Certified NO x

(g/bhp-hr) Certified PM 10 (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 calcul ated the NO x ZMRs and DRs of both HHDDT and MHDDT for 2006-2011 model years by weight ing the ZMRs and DRs of the corresponding model year groups. Staff did not rev ise the PM emission factors for HHDDT and MHDDT. As can be seen in Table 18, the s ales 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 r ates for the 2006 model year is negligible.G-40

In Table 19 we show the current and revised NO x ZMR and DR of both HHDDT and MHDDT for 2006 to 2011 model years.
Table 19. NO x

Emission Factors for 2006-2011 MY Heavy-Duty Diese l 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 0.0 765
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 (CO 2 ) Emission Rates Reducing carbon dioxide (CO 2 ) emissions is a primary goal of the State of Calif ornia. CO 2 emissions are generated through the combustion of fuels and in particular by combustion of fuels in trucks and buses that operat e in California. CO 2 emissions are a function of engine size, load, speed, miles travele d, and many other factors. This section describes how the staff derived CO 2 emission rates to develop emissions inventories in support of the proposed regulation.

Although EMFAC2007 provides CO 2 emission rates for heavy duty diesel trucks, it assumes them to be constant regardless of model yea r, technology group, activity and other factors. To develop more finely resolved CO 2 emission rates for trucks, staff used fuel economy data as a surrogate for CO 2 since there is a larger database of fuel economy data available for trucks than CO 2 data. Fuel economy estimates were converted to CO 2 emission rates based on the carbon content of dies el fuel, as discussed in more detail later in this section.

In this analysis we have evaluated available data t o determine how improvements in engine technology and increasingly stringent criter ia pollutant emission control requirements have affected the fuel economy of HHDD T and, consequently, CO 2 emission rates. Staff has recently reviewed the fo llowing 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: Mode l Year 1988-2003

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

• Consent Decree, in-use study by West Virginia Univ ersity: Model Year 1994- 2003G-41

We chose to not use the Department of Energy (DOE) fuel economy data directly since those are national data and therefore not represent ative of the truck fleet or driving conditions found in California. Also, the DOE dat a do not provide information on how fuel economy and CO 2 emission rates vary as a result of technology. DO E data were used instead as an independent check on our technic al analysis.

Our technical analysis shows differences in fuel ec onomy 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 grou p, as well as the assumptions that are embedded in those estimates.

Table 20. Proposed Fuel Economy Values for HHDDT O perating 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 Comb ustion 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 avai lable 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 mechan ical control and electronic control from CRC E55/59 data

o Assume all model year mechanical control engines h ave 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 Engin es

o MY 1993-1998

o Higher fuel economy and higher NO x than mechanical control engines.

o Model year fuel economy of 5.95 mpg does not refle ct 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 NO x control

• MY 2003-2006:

o 5% gain in fuel economy

o Introduction of advanced combustion technologies i ncluding exhaust gas recirculation (EGR)

• MY 2007-2009:

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

o 3% fuel penalty for meeting 2007 emissions standar ds

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 standard s in CY 2009

• MY 2010-2012:

o Introduction of selective catalytic reduction (SCR ) for NO x control

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

o 90% of engines would meet 0.2 g/bhp-hr NO x standards in 2010 and 2011

o 10% of engines would meet 0.5 g/bhp-hr NO x standards with fuel economy of 5.58 mpg in 2010 and 2011

o 100% of engines would meet 0.2 g/bhp-hr NO x standards in 2012 (ARB, 2008c)G-43

We applied the fuel economy estimates for each mode l year across each inventory category to develop a composite fuel economy by cal endar year. Figure 18 compares fuel economy estimates by calendar year against val ues derived from DOE, IFTA, and EMFAC2007. This figure shows that HHDDT fuel econo my is estimated to improve over time due initially to the introduction of elec tronically controlled engines and later due to the introduction of SCR-equipped engines. T hese results compare remarkably well with EMFAC, DOE, and IFTA data. For example, the composite fuel economy reported by all trucks operating in California in t he 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 var ying 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 co unterparts, are estimated to have slightly lower fuel economy (3% to 5%) than interst ate trucks.

Figure 18. Fleet Average Fuel Economy for Trucks O perating 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 YearFleet Average Fuel Economy (mpg)

EMFAC 2007 (~5.4 mpg) California IFTA Data

DOE Data Heavy-duty interstate trucksG-44

To convert fuel economy data to CO 2 emission rates, staff used the following methodology:

• estimate the fuel consumption, in gallons, for eac h truck category based upon the model year fuel economies and model year distri bution

• multiply the estimated fuel consumption by the car bon content of diesel fuel (2,778 g carbon/gallon diesel (USEPA (2008)) to est imate the carbon emitted by each truck category

• multiply the estimate of carbon emitted by each ca tegory by the ratio between the molecular weight of CO 2 and the molecular weight of carbon (44/12) to estimate the CO 2 emissions from each category if 100% efficient

• assume 99% efficiency, multiplying the CO 2 emitted at 100% efficiency by 0.99, to estimate the final CO 2 emissions estimate for each category.

The application of this process is equivalent to ap plying an estimate of 22.2 lbs CO 2 /gallon diesel fuel (10.08 kg CO 2 /gallon diesel fuel) to the estimate of fuel consum ed 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 abo ve. CO 2 emission rates for buses were taken directly from EMFAC2007.

4. Final emissions rates

Staff estimated the emission rates for each truck i nventory 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 ave rage 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; th ese, too, are shown in the database provided with this appendix.

F. Forecasting the Baseline Inventory

In this section we describe the methods used to est imate the growth of future truck populations and truck VMT in the emissions inventor y.

1. Growth In modeling the emissions for future years, staff n eeded to estimate the amount of vehicular travel in future years. The emissions ar e 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-spec ific 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 pu blic 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 e stimated the VMT associated with the drayage trucks at each of these ports/rail yards to grow at a slightly faster rate. We estimated the population of drayage trucks at ot her 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 usin g 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 g rowth 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 stak eholders. The average annual mileage accrual of each agricultural truck was assu med 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 EMFA C2007 which is 2.66% for heavyheavy duty trucks and 1.62% for medium-heavy duty t rucks. 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 V MT within each truck category.

Table 21. Annual VMT Growth Rates Projected by Cat egory, 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, differen t from that currently assumed in EMFAC2007. Staff regressed national VMT data from the Bureau of Transportation Statistics (BTS) against the US Gross Domestic Prod uct (GDP) for the trucking industry nationwide, which was also released by the BTS. Th is relationship was then projected with a prediction of the future trucking GDP to est imate the future VMT, again on a federal level. The future nationwide trucking GDP was predicted by extending the relationship regressed earlier between the nationwi de trucking GDP and the employment in the transportation sector predicted i n the State of California Economic Forecast for the Sacramento Forecast Project. We s how 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 2025VMT (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 f or 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 decid ed 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, outs ide of regulatory impacts, the age distribution of vehicles within each inventory cate gory would remain constant. Thus, the fraction of a vehicle class represented by vehicles of a certain age would remainG-47

constant; for example, the fraction of MY 2000 vehi cles in the category in calendar year 2008 was assumed to be equivalent to the fraction o f 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 ca use trucks from one year to be more expensive than, and thus preferred to, trucks of an other year. A trucking firm might wish to not buy a truck with unproven technology; t hus, they may delay their purchase for several years until the technology is more prov en 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 “p re-buy” is less than that from general economic trends, we have attempted to incorporate t his behavioral pattern in our assessment of model year distribution for each inve ntory category.

To approximate the sales of trucks in future years, and thus the impact to the age distribution of trucks, staff first estimated the h istoric annual nationwide truck sales as a function of the historic nationwide GDP associated with transportation activities. Staff used data regarding nationwide truck sales, as esti mated by the website WardsAuto.com for 1985-2007, and GDP, as estimated for transportation services nationwide by the US Department of Commerce’s Burea u 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 mode l 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 correlat ion that also existed between GDP and time lessened the positive impact of time when truc k 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 industr y could foresee the decline in economic activity and deferred the purch ase of new trucks before the decline was experienced.G-48

Staff then approximated the historic relationship b etween the historic nationwide transportation GDP and the historic California tran sportation employment, as estimated by a UCLA business forecast released in July 2007 ( UCLA, 2007). We assumed that the growth rate for transportation employment in Ca lifornia was approximated by the growth rate for nationwide transportation GDP. We used this relationship with the projections to 2020 for California transportation e mployment from the UCLA forecast to project the nationwide transportation GDP until 202 0 as shown in Figure 20.

Figure 20. Nationwide Transportation GDP Trends: H istoric 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 2025Nationwide Transportation GDP

<-- Actual Projected -->G-49

As a first step toward projecting truck sales, staf f developed a regression model between nationwide transportation-related GDP and h istoric 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 t he truck sales initially predicted from this model beyond 2006.

Figure 21. The Modeled Relationship between Nation wide 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 2015Truck Sales Actual truck sales

Predicted truck salesG-50

The modeled relationship did not account for what a ppeared 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 tru ck sales were lowered. This correction factor, the adjustment made to the estim ate resulting from the model, was equivalent in proportion to the difference between earlier estimates and corresponding actual results. Staff then adjusted truck sales be tween 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 Nation wide 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 2020Truck Sales Actual truck sales

Predicted truck sales Adjusted truck sales

Transportation employment within California was alr eady projected to grow at a slightly slower rate after 2015; hence sales for this time p eriod were projected to grow at a slower rate.G-51

Finally, staff received additional data regarding t ruck sales to date in 2008. These sales were even lower than those projected by the adjuste d 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 Year s

0 50,000 100,000 150,000 200,000 250,000 300,000

1985 1990 1995 2000 2005 2010 2015 2020Truck 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 pred iction 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 popu lations, staff calculated the ratio between sales of trucks in one model year and the e xpected number of sales if no factor (e.g. GDP) had caused variation. We show the actua l 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 -12 e 0.019E-02*year

0 50,000 100,000 150,000 200,000 250,000 300,000

1985 1990 1995 2000 2005 2010 2015 2020Truck 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 sa me 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 cu mulative 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 dra yage regulation was used.G-53

G. Statewide Baseline Activity and Emissions We discussed the population of each vehicle type ea rlier in the Base Year Population and Activity section, but we provide a summary tabl e (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 state s. 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 Califor nia 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 est imated number of trucks and buses operating in California that fall under this propos ed 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 bu s population represented by each category. The California share of VMT represents t he 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 c an be small for interstate categories. The share of VMT in California represents the contr ibution made by each inventory category to the total truck and bus VMT estimated f or California.

As shown in Table 22, heavy-heavy duty diesel truck s registered outside California account for the majority of trucks traveling in Cal ifornia. Because these trucks do not travel exclusively in California, however, their po rtion of VMT in California, at 28.5%, is not as large as their representation among the popu lation (52.3%). In addition, because trucks in this inventory category tend to be newer than trucks in other categories, theirG-54

emission rates are lower and their contribution rel atively less than from their in-state counterparts.
1. Base Year Age Distributions across Inventory Cat egory In an earlier section, we provided the age distribu tion of trucks within each inventory category. In Figure 25 we compare the age distribu tions for the different categories. (Note that trucks from non-neighboring states, beca use 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 tr actors 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 tr ansferred from the out-of-state and IRP categories in their later years. The oldest tr ucks represent the agriculture category and the drayage category; these trucks tend to trav el, on average, the shortest distances. Staff used this age distribution when e stimating 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 AgePopulation

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000Population (NNOOS)

HH Drayage HH CA IRP HH Instate Tractor HH NOOS HH Agriculture HH NNOOS Trucks

Figure 26 gives a similar representation of the tru cking 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 ageG-55

distribution for single-unit trucks does not vary s o much between categories as the age distribution does for combination trucks. With reg ard to single-unit categories in particular, medium-heavy instate trucks are relativ ely 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 AgePopulation

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000Population (MH Instate)

HH Single Unit Trucks MH Utility Trucks HH Utility Trucks MH Agriculture Buses MH Instate TrucksG-56

2. Base Year and Forecasted Population by Inventory Category In Figure 27 we show the distribution of trucks ass umed to visit California in 2008 and 2020. In both years the majority of individual tru cks represent one of four categories: heavy-heavy trucks registered outside California, C alifornia-registered heavy-heavy trucks in the International Registration Program, h eavy-heavy tractors registered in California, and medium-heavy trucks registered in C alifornia. In a later section we show the distribution of California VMT between the cate gories; for two primary reasons, the distribution of VMT differs slightly from the distr ibution of the unique trucks themselves. First, trucks registered outside California do not travel entirely within California. Second, trucks within differing categories typicall y do not exhibit the same travel patterns with regard to distance.

Figure 27. A Comparison of Truck Population by Cat egory (2008 and 2020)

0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000

2008 2020Truck 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) HH Tractor (Instate) MH In-state MH In-stateG-57

3. Comparing Accrual Rates by Inventory Category In Figure 28 we show the variation in VMT accrual r ates between inventory categories in California. Note that this figure shows only th e VMT estimated to be accrued within California’s borders and does not include the VMT e stimated to be accrued elsewhere. This is particularly relevant in the context of tru cks registered in states that do not border California, of which more than 90% of the an nual VMT are assumed to be accrued outside California. In the context of virt ually all heavy vehicles except drayage trucks, the annual VMT for a vehicle is estimated t o decrease with the age of the vehicle; drayage trucks are assumed to maintain a s teady 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

AgeCA Accrual (miles)

HH CA IRP HH Instate Tractor HH NNOOS Trucks HH NOOS HH Agriculture Other Ports Trucks HH POAK Trucks HH POLA\LB TrucksG-58

In Figure 29 we show a similar distribution in accr ual 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 truck s used for agriculture. The accrual rates estimated for heavy-heavy trucks from the uti lity 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 AgeCA 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 200 5 and 2025. Figure 30 shows the aggregate VMT for each year; the table lists the da ily VMT for each truck category in select years. The five most significant contribut ors 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 repr esent over 81% of all VMT associated with bus and truck travel in California in 2008. T hough not necessarily in the same order, we project these five categories to remain t he largest heavy duty truck VMT contributors in 2025, collectively accounting for o ver 83% of all heavy duty truck VMT driven within California’s borders. (The HHDDT fro m states not neighboring California are projected to represent the single largest share in 2025 at 22.1%.)

Figure 30. California Vehicle Miles Traveled by Tr uck Category

0 10 20 30 40 50 60 70 80 90200520062007200820092010201120122013201420152016201720182019202020212022202320242025VMT (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 DrayageG-60

Table 23. Distribution of Estimated Daily VMT in S elect 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 associat ed with each category between 2005 and 2025. As the figure shows, the share of V MT represented by drayage traffic is expected to grow at the expense of other categor ies (e.g. MHDDT instate) due to the relatively higher growth in goods movement related truck categories.

Figure 31. Share of California Vehicle Miles Trave led by Truck Category

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%200520062007200820092010201120122013201420152016201720182019202020212022202320242025Daily 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 DrayageG-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 categor ies 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 PTOG-62

In Figure 32 we show the distribution of annual tru ck VMT graphically. Four categories again represent the majority of truck VMT: heavy-he avy 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 Cat egory (2008 and 2020)

0 5 10 15 20 25 30

2008 2020Annual 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 NO x , 33.1 tons per day PM 2.5 , and 108,429 tons per day CO 2 . We show these data in Table 25.

Table 25. Statewide Emissions (2008)

CY Category Truck

Population Truck CA

VMT/day NO x

(tons/day) PM 2.5

(tons/day) CO 2

(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-stateG-63 b) Baseline Emissions by Inventory Category We provide in Table 26 the baseline heavy duty truc k and bus emissions inventory for the entire state of California in 2008, broken down by inventory category.

Table 26. Statewide Emissions by Inventory Categor y (2008)

CY Inventory Category Truck

Population Truck CA

VMT/day NO x

(tons/day) PM 2.5

(tons/day) CO 2

(tons/day)
2008 HH Out-of-State 492,340 16,142,326 221.9 8.0 3 2,766 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 19,06 7 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 f or 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 NO x

(tons/day) PM 2.5

(tons/day) CO 2

(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 43 6.4 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 31 6.1 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 6 0.5

1 truck/above 7500 miles 15,980 2,198,998 41.85 1.4 9 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 93 3.0 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 2.7 1 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. 8G-64

Inventory Category Fleet Size Truck Population Truck CA

VMT/day NO x

(tons/day) PM 2.5

(tons/day) CO 2

(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 10 7.2 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 56 3.7 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 37 3.0 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 13 8.9

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 0.4 9 1,301.8 Ag non specialty lower VMT 5,258 82,489 1.89 0.10 1 78.5 Ag non specialty midrange VMT 1,442 62,773 1.27 0.0 6 128.9 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 1.4 8 5,672.2 1 truck/below 5000 miles 11,592 80,942 1.20 0.06 12 7.6 2 truck/above 5000 miles 16,710 1,244,223 12.65 0.4 7 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 12 2.7 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 7 2.6 Ag non specialty midrange VMT 1,946 78,768 0.89 0.0 4 119.5 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 33.07 108,429.2G-65

We show in Table 28 the baseline emissions inventor y for the entire state of California, broken down only by fleet size and annual mileage t hresholds.

Table 28. Baseline Emissions by Fleet Size and Mil eage Threshold (2008)

CY Fleet Size Truck Population Truck CA

VMT/day NO x

(tons/day) PM 2.5

(tons/day) CO 2

(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 0.1 7 331 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 23 1 2008 Unspecified 30,597 1,790,835 28.5 1.29 3,549 2008 All 940,667 57,009,437 859.3 33.07 108,429G-66

We show the emissions as estimated with the baselin e scenario for each category of pollutant in the following figures: NO x in Figure 33, PM 2.5 in Figure 34, and CO 2 in Figure 35. As shown, the statewide emissions for NO x and PM 2.5 are expected to decrease in the absence of regulation, due to the natural repla cement of older trucks with newer, cleaner trucks. Baseline CO 2 emissions, however, are projected to increase sinc e improvements in fuel economy are not expected to ke ep pace with increased heavy duty truck VMT. ARB is proposing to improve fuel e conomy and reduce CO 2 emissions from heavy duty trucks in future years through othe r programs and technologies, including increased usage of aerodynamic fairings a nd tires of lower rolling resistance.

Figure 33. California Statewide NO x emissions from Trucks, Baseline 2008 - 2023

0 100 200 300 400 500 600 700 800 900 1,000

2 0 0 7

2 0 0 8

2 0 0 9

2 0 1 0
2 0 1 1

2 0 1 2

2 0 1 3

2 0 1 4

2 0 1 5

2 0 1 6

2 0 1 7

2 0 1 8

2 0 1 9

2 0 2 0

2 0 2 1

2 0 2 2

2 0 2 3

2 0 2 4 N O x e m i s s i o n s ( t o n s / d a y )G-67

Figure 34. California Statewide PM 2.5 Emissions from Trucks, 2008-2023

0 5 10 15 20 25 30 35

2 0 0 7

2 0 0 8

2 0 0 9

2 0 1 0
2 0 1 1

2 0 1 2

2 0 1 3

2 0 1 4

2 0 1 5

2 0 1 6

2 0 1 7

2 0 1 8

2 0 1 9

2 0 2 0

2 0 2 1

2 0 2 2

2 0 2 3

2 0 2 4 P M 2 . 5 e m i s s i o n s ( t o n s / d a y )

Figure 35. California Statewide CO 2 Emissions from Trucks, Baseline 2008-2023

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000

2

0 0 7

2 0 0 8

2 0 0 9

2 0 1 0
2 0 1 1

2 0 1 2

2 0 1 3

2 0 1 4

2 0 1 5

2 0 1 6

2 0 1 7

2 0 1 8

2 0 1 9

2 0 2 0

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2 0 2 4 C O 2 e m i s s i o n s ( t o n s / d a y )G-68

H. Statewide Truck and Bus Regulation Benefits The proposed Statewide Truck and Bus Regulation wou ld initially require older vehicles to install diesel particulate filters. Several yea rs later, the regulation would require operators to either purchase a newer compliant vehi cle to replace an older noncompliant vehicle or retrofit the older non-complia nt vehicle with emissions controls that would enable each vehicle to comply with regulatory emission standards. Under the proposed regulation, a fleet operator may choose am ong multiple compliance options on a per-pollutant and per-calendar year basis. Op tions include: (1) best available control technology (BACT) schedule; (2) fleet avera ge requirements; (3) percent limit requirements; (4) low mileage thresholds with alter native compliance schedules; (5) small fleet compliance options; (6) regional compli ance options; and (7) other special provisions.

To estimate the emissions benefits generated by the proposed regulation, one must understand how individual fleet operators may choos e to comply with the regulation. Each operator’s response may ultimately depend on t he age, body type, and other characteristics of vehicles in each fleet and the r elationship between vehicles in each fleet to the inventory categories. One of the ways we assess potential compliance patterns is by evaluating previous vehicle buying p atterns 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 s cenario with regulation. We based the cost and economic model, described in greater d etail 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 patt erns 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 seve ral years in long-haul operation, trucks tend to be sold to regional fleets, and a fe w 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, w e also evaluated age distributions for each fleet size and mileage threshold group within each inventory category.

Our benefit calculations are also fundamentally bas ed on the idea that newer vehicles drive more than older vehicles, but that the regula tion will not affect the number of vehicle miles traveled within California. As a res ult, as new vehicles are purchased due to regulatory requirements, we redistribute VMT acr oss 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 methodolo gy that separated vehicles into five groups: (a) high mileage large fleets, (b) high mil eage small fleets, (c) low mileageG-69

vehicles in large or small fleets, (d) agricultural trucks, and (e) buses. For each group we developed a compliance schedule based upon our b est estimate of anticipated purchase decisions. Our compliance schedules devel oped for each inventory category and compliance group assumed that larger, newer fle ets will comply with the regulation by purchasing new or near-new vehicles and that whe re possible fleets will choose to avoid installing retrofits (especially on mechanica lly controlled engines that are more costly to control) – instead opting to purchase 200 7 standard compliant trucks.

a) High Mileage Trucks in Fleets of Four or Greater Large, high-mileage truck fleets are well-represent ed in the cost model. The cost model uses previous purchase behavior to predict future p urchases for regulatory compliance by fleet, based on the compliance schedules availab le to these fleets. We assigned an inventory category to each truck in each fleet so t hat 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 deve lop a compliance schedule for the four categories.G-70

We show those compliance schedules in Table 29. A s shown in the table, we assume large fleets will choose to replace, rather than re trofit, pre-2003 model year vehicles to meet PM BACT requirements. We do assume fleets wil l 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 co mpliant replacement vehicles, they will purchase a vehicle that is four or five year o ld. This assumption is a schematic representation that some fleets will choose to purc hase 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% 2019G-71

Heavy-Heavy Duty Diesel In-State Single-Units and P ower-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% 2019G-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 exe mpt from performance requirements through 2011. In 2012 a small fleet must upgrade i ts first truck to a maximum emission rate equivalent to a 2004 engine with a retrofit DP F. Other vehicles in small fleets must be upgraded between 2013 and 2022. Using this info rmation, 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 2015G-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 lessG-74

than 5,000 miles/yr) are allowed to delay complianc e with turnover requirements until 2020. Using this information we applied the compli ance schedule shown in Table 31.

Table 31. Compliance Assumptions for Low Mileage T rucks 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 scenar io, high mileage non-specialty trucks in agricultural fleets, defined as pre-1996 model y ear 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 th an 25,000 miles per year, must comply with regulatory provisions. Non-specialty t rucks driving fewer miles are not required to install retrofit DPFs and are not requi red to meet turnover requirements until 2016 or 2022 depending on the number of miles trave led per year. In 2016, the mileage threshold is reduced to 10,000 miles per year. Tru cks above that threshold which had not previously been complying with regulatory provi sions 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 mode l 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 tr ucks
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 thres hold
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 2012G-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 tha t are privately owned are assumed to follow BACT provisions in the proposed regulatio n. Due to the cost of replacing these vehicles we assume bus operators will achieve compl iance with the least-cost option; this typically involves the oldest compliant vehicl e available. Under the proposed regulation, school buses are required to install DP Fs but are not required to meet NO x emission standards. These requirements apply in ad dition to previous regulatory requirements developed under the Lower Emissions Sc hool 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 2007G-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 NO x emissions reduction estimated to result from the proposed regulation. We estimate the greatest abso lute NOx emissions benefit to be achieved in calendar year 2014, with a reduction of 123.7 tons NO x /day relative to baseline emissions. In Figure 37 we show the emiss ions reduction estimated to result from the regulation, in terms of PM 2.5 . We anticipate the greatest emissions reduction t o be achieved for PM 2.5 in 2013, with a reduction of 13.6 tons PM 2.5 /day relative to baseline emissions. We estimate the CO 2 emissions benefit resulting from the regulation to be negligible, with the greatest redu ction of 570.8 tons CO 2 /day occurring in 2023. In some years the CO 2 emissions are estimated to slightly increase due t o the decreased fuel efficiency resulting from the techno logies used to reduce NO x and PM 2.5
. We show the emissions reduction estimate for CO 2 in Figure 38.

Figure 36. Statewide NO x Emissions Estimates with Regulation

0 100 200 300 400 500 600 700 800 900 1,000

2 0 0 7

2 0 0 8

2 0 0 9

2 0 1 0
2 0 1 1

2 0 1 2

2 0 1 3

2 0 1 4

2 0 1 5

2 0 1 6

2 0 1 7

2 0 1 8

2 0 1 9

2 0 2 0

2 0 2 1

2 0 2 2

2 0 2 3

2 0 2 4 N O x e m i s s i o n s ( t o n s / d a y )

Baseline

With RegulationG-78 Figure 37. Statewide PM 2.5 Emissions Estimates with Regulation

0 5 10 15 20 25 30 35

2 0 0 7

2 0 0 8

2 0 0 9

2 0 1 0
2 0 1 1

2 0 1 2

2 0 1 3

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2 0 1 8

2 0 1 9

2 0 2 0

2 0 2 1

2 0 2 2

2 0 2 3

2 0 2 4 P M 2 . 5 e m i s s i o n s ( t o n s / d a y )

Baseline

With Regulation

Figure 38. Statewide CO 2 Emissions Estimates with Regulation

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000

2

0 0 7

2 0 0 8

2 0 0 9

2 0 1 0
2 0 1 1

2 0 1 2

2 0 1 3

2 0 1 4

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2 0 1 6

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2 0 1 9

2 0 2 0

2 0 2 1

2 0 2 2

2 0 2 3

2 0 2 4 C O 2 e m i s s i o n s ( t o n s / d a y ) Baseline

With RegulationG-79 b) Statewide Emissions Benefits by Inventory Catego ry In Table 34 we provide statewide NO x 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 g reatest percent NO x reduction relative to their baseline, achieving 45% reductions in 2014 and 2020. (The reduction achieved by the proposed regulation with regard to drayage t rucks is negligible for NO x and other pollutants in 2014 and 2020 as a result of the ARB drayage truck regulation adopted in 2007, which will already require significant emissi ons reductions prior to 2014 (ARB, 2007a; ARB, 2007b); the regulation proposed herein requires further reductions after 2020). When considering all truck categories in ag gregate, the largest share of the overall NO x

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 NO x Future Emissions Reductions by Inventory Category, 2014 and 2020 (tons/day)

2014 2020 Inventory Category Base 1 Reg 2 Reduction 3 Share 4 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% 10 0.0%
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 PM 2.5 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 PM 2.5 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 r elative to individual category baseline emissions. When considering all truck categories i n aggregate, the largest share of the overall PM 2.5 reduction to be achieved by proposed rule should b e provided by in-stateG-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 PM 2.5 Future Emissions Reductions by Inventory Category, 2014 and 2020 (tons/day)

2014 2020 Inventory Category Base 1 Reg 2 Reduction 3 Share 4 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% 1 3.5% 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 cat egoryG-81

In Table 36 we provide statewide CO 2 reductions achieved by the proposed regulation in calendar years 2014 and 2020 for each inventory category. The overall CO 2 reduction for each category is negligible, with non e exceeding 1% and some actually increasing CO 2 emissions. When considering all the categories in aggregate, the largest share of the overall CO 2 reduction in 2014 is again represented by the trac tors, 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 CO 2 Future Emissions Reductions by Inventory Category, 2014 and 2020 (tons/day)

2014 2020 Inventory Category Base 1 Reg 2 Reduction 3 Share 4 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 cat egoryG-82

In Table 37 we show the distribution of all emissio ns reductions anticipated to result from the proposed regulation. We expect the larges t reductions in NO x and PM 2.5 will come from trucks registered in California, with hea vy-heavy instate trucks providing the greatest reductions followed by medium-heavy in-sta te trucks and single-unit trucks. These three categories alone represent over 74% of the overall NO x and 67% of the overall PM 2.5 emissions reductions anticipated between 2008 and 2 023.

Table 37. Distribution of Total Future Emissions R eductions across Inventory Category , 2008-2023
Inventory Category NO x

Share PM 2.5

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 NO x reductions for 2014 and 2020 by truck fleet size. As shown in the table, we exp ect 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 o verall 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 nea rly 45% of the NO x emissions reductions anticipated in 2014, and more than 47% o f the reductions anticipated in 2020, will come from fleets with three or fewer veh icles. These projections result largely from the fact that smaller fleets tend to have on a verage older trucks than do larger fleets. In Table 39 we show the future NO x baseline emissions and reductions as a function of inventory category and fleet size.

Table 38. California Statewide NO x 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 NO x Future Emissions Reductions by Inventory Category and Fleet Size, 2014 and 2020 (tons/day)

2014 2020 Inventory Category Fleet Size Base Reg Red Share Ba se 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 Ba se 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% 2 1.91 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% 3 3.68 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% 9.9 0 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% 8.5 4 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% 1 4.54 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% 1 7.76 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% 0.1 6 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 PM 2.5 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 theG-85

respective thresholds included for application of t he regulation. The only trucks not affected are agricultural trucks that are less-util ized. When considering all fleets in aggregate, the largest share of the overall reducti on is again represented first by fleets operating more than three trucks, with the second l argest share represented by singletruck fleets. The reduction rates, again, are grea test for fleets of 3 or fewer vehicles. We again expect that nearly 45% of the PM 2.5 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 r esult largely from the fact that smaller fleets tend to have older vehicles than do larger f leets. In Table 41 we break down the future PM 2.5 baseline emissions and reductions further as a fun ction of inventory category and fleet size.

Table 40. California Statewide PM 2.5 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 PM 2.5 Future Emissions Reductions by Inventory

Category and Fleet Size, 2014 and 2020 (tons/day)

2014 2020 Inventory Category Fleet Size Base Reg Red Share Ba se 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% 0.0 1 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% 0.8 2 0.65 -21.0% 3.3% HH CAIRP

4+ truck/below 7500 miles 0.01 0.00 -82.0% 0.1% 0.0 1 0.00 -70.0% 0.1%G-86 2014 2020

Inventory Category Fleet Size Base Reg Red Share Ba se Reg Red Share

1 truck/above 7500 miles 2.36 0.40 -82.8% 15.2% 1.4 5 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% 0.0 4 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% 0.5 0 0.20 -60.9% 5.9% HH Single Unit

4+ truck/below 7500 miles 0.05 0.01 -83.6% 0.3% 0.0 4 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% 0.6 3 0.29 -54.1% 6.7% MH instate

4+ truck/below 5000 miles 0.05 0.01 -82.6% 0.3% 0.0 3 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% 0.0 1 0.00 -37.3% 0.0% MH Interstate

4+ truck/below 5000 miles 0.00 0.00 -80.2% 0.0% 0.0 0 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 CO 2 reductions for 2014 and 2020 by truck fleet size. We anticipate a slight increa se for CO 2 emissions in 2014 and a slight decrease for emissions in 2020. As noted ea rlier, these proposed regulations are not directed toward the reduction of CO 2 emissions. When pooling the fleets together, the largest share of the overall reduction is repre sented by larger fleets and single-truck fleets in both 2014 and 2020.

Table 42. California Statewide CO 2 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% 8,92 3 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% 9 2,151 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 24 5 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 CO 2 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% 1,92 5 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% 6,18 3 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% 2,28 3 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% 1,27 0 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% 1 3,073 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% 8,1 58 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% 2,44 3 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% 1,43 9 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% 2,10 3 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% 1,1 41 1,136 -0.4% 10.8% Ag non specialty lower VMT 183 183 0.0% 0.0% 175 17 5 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% 6,72 1 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% 2,20 3 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% 1,31 0 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% 1 2,087 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

I. References
ARB, 2001. California Air Resources Board. Lower-Emission School Bus Program

. April 2001. Available at: http://www.arb.ca.gov/msprog/schoolbus/finalguide.p df

ARB, 2003. California Air Resources Board. Appendix E. Revised Solid Waste Collection Vehicle Emissions Inventory . August 11, 2003. Available at: http://www.arb.ca.gov/regact/dieselswcv/appe2.pdf

ARB, 2004a. California Air Resources Board. Final Statement of Reasons for Rulemaking, Including Summary of Comments and Agenc y Responses: Public Hearing to Consider the Adoption of Proposed Particulate Ma tter Control Measure for On-Road Heavy-Duty Diesel Fueled Residential and Commercial Solid Waste Collection Vehicles . May 7, 2004. Available at: http://www.arb.ca.gov/regact/dieselswcv/fsor.pdf

ARB, 2004b. California Air Resources Board. Final Statement of Reasons: Adoption of the Heavy-Duty Diesel Engines Software Upgrade Regu lation (Chip Reflash)

. December 2004. Available at: http://www.arb.ca.gov/regact/chip04/fsor.pdf

ARB, 2005. California Air Resources Board. Appendix B – Public and Utility Fleet Vehicles Emissions Inventory . December 8, 2005. Available at: http://www.arb.ca.gov/regact/dpmcm05/appb.pdf

ARB, 2006a. California Air Resources Board. EMFAC 2007. Available at: http://www.arb.ca.gov/msei/onroad/latest_version.ht m

ARB, 2006b. California Air Resources Board. Revision of Heavy Heavy-Duty Diesel Truck Emission Factors and Speed Correction Factors . October 20, 2006. Available at: http://www.arb.ca.gov/msei/onroad/techmemo/revised_ hhddt_emission_factors_and_sp eed_corr_factors.pdf

ARB, 2006c. California Air Resources Board. Revision of Heavy Heavy-Duty Diesel Truck Emission Factors and Speed Correction Factors - Amendment

. October 20, 2006. Available at: http://www.arb.ca.gov/msei/onroad/techmemo/modifica tion_hhddt_speed_and_speed_c orr_factors.pdf

ARB, 2006d. California Air Resources Board. Final Statement of Reasons – Proposed Diesel Particulate Matter Control Measure for On-Ro ad Heavy-Duty Diesel Fueled Vehicles Owned or Operated by Public Agencies and U tilities . October 2006. Available at: http://www.arb.ca.gov/regact/dpmcm05/revfsor.pdf

ARB, 2006e. California Air Resources Board. EMFAC Modeling Change Technical Memo: Revision of Heavy Heavy Duty Diesel Truck Emi ssion Factors and Speed Correction Factors . October 2006. Available at: http://www.arb.ca.gov/msei/onroad/techmemo/revised_ hhddt_emission_factors_and_sp eed_corr_factors.pdfG-90

ARB, 2006f. California Air Resources Board. Amendment to EMFAC Modeling Change Technical Memo: Revision of Heavy Heavy Duty Diesel Truck Emission Factors and Speed Correction Factors . October 2006. Available at: http://www.arb.ca.gov/msei/onroad/techmemo/modifica tion_hhddt_speed_and_speed_c orr_factors.pdf

ARB, 2006g. California Air Resources Board. EMFAC Modeling Change Technical Memo: Redistribution of Heavy-Heavy Duty Diesel Tru ck Vehicle Miles Traveled in California . September 2006. Available at: http://www.arb.ca.gov/msei/onroad/techmemo/on-2006- 05.pdf

ARB, 2007a. California Air Resources Board. Technical Support Document: Initial Statement of Reasons for the Proposed Rulemaking – Proposed Regulation for Drayage Trucks . October 2007. Available at: http://www.arb.ca.gov/regact/2007/drayage07/drayiso r.pdf

ARB, 2007b. California Air Resources Board. Appendix B: Emissions Estimation Methodology for On-Road Diesel-Fueled Heavy-Duty Dr ayage Trucks at California Ports and Intermodal Rail Yards . October 2007. Available at: http://www.arb.ca.gov/regact/2007/drayage07/appbf.p df

ARB, 2007c. California Air Resources Board. 2007 Almanac . August 2007. Available at: http://www.arb.ca.gov/app/emsinv/fcemssumcat2007.ph p

ARB, 2008a. California Air Resources Board. Heavy-Duty Diesel Vehicles Survey

. Available at: http://www.arb.ca.gov/msprog/onrdiesel/onroad_surve y/index.php

ARB, 2008b. California Air Resources Board. Revised 2006-11 HDDT ZM&DR 08152008

”. August 2008.

ARB, 2008c. California Air Resources Board. Update of 2006-2011 Model Year Heavy Heavy-Duty and Medium Heavy-Duty Diesel Truck Emiss ion Factors . August 2008.

Battelle, 1999. Federal Highway Adminstration. Heavy-Duty Truck Activity Data . April 1999. Available at: http://www.fhwa.dot.gov/ohim/tvtw/final.pdf

California Department of Finance, 2008. Top Countr ies Ranked by its Gross Domestic Product, California's World Ranking. July 2008. A vailable at: http://www.dof.ca.gov/HTML/FS_DATA/LatestEconData/F S_Misc.htm http://www.dof.ca.gov/HTML/FS_DATA/LatestEconData/d ocuments/BBRANK.XLS

California Department of Motor Vehicles, 2001. Com mercial Vehicle Registration Act of 2001. 2001. Available at: http://www.dmv.ca.gov/commercial/cvra.htmG-91

California Department of Transportation, 2001. Cal ifornia Stock, Motor Vehicle, Travel, and Fuel Forecast. November 2001. Available at: http://www.dot.ca.gov/hq/tsip/smb/documents/mvstaff /mvstaff01.pdf

California Highway Patrol, 2006. 2005 CHP Safety Ce rtification Database. Provided by Mike Chaffee, California Highway Patrol, through di rect correspondence.

California State Board of Equalization, 2008. Spec ial Taxes – Fuel Taxes. Available at: http://www.boe.ca.gov/sptaxprog/mciftamain.htm

California State University, Sacramento, 2008. Sac ramento Forecast Project: State of California Economic Forecast. July 2008. Available at: http://www.csus.edu/indiv/j/jensena/sfp/ca/calif.ht m
Cambridge Systematics, 2008. San Joaquin Goods Mov ement Study – Phase III. Prepared for Fresno Council of Governments. More i nformation is available at: http://www.fresnocog.org/document.php?pid=42

Coordinating Research Council, Inc., 2007. HEAVY-D UTY Vehicle Chassis Dynamometer Testing for Emissions Inventory, Air Qu ality Modeling, Source Apportionment and Air Toxics Emissions Inventory. August 2007. Available at: http://www.crcao.com/reports/recentstudies2007/E-55 -59/E55_59_Final_Report_23AUG2007.pdf

International Registration Plan Incorporated, 2008. General Questions. Available at: http://www.irponline.org/About/GeneralInformation

Lutsey, Nicholas, Institute of Transportation Studi es, University of California at Davis, 2008. Assessment of Out-of-State Heavy Heavy Duty Truck Activity Trends in California – Final Report. March 2008. Available at: http://www.arb.ca.gov/research/apr/past/04-328.pdf

Southern California Association of Governments, 200 3. Year 2000 Model Validation and Summary – Regional Transportation Model. April 2003. Available at: http://www.scag.ca.gov/modeling/mtf/pdf/1_35.pdf

TIAX LLC., 2003. California Public Fleet Heavy-Duty Vehicle and Equipment Inventory Final Report. March 2003. Available at: http://www.arb.ca.gov/msprog/publicfleets/archive05 .htm

UCLA, 2007. UCLA Anderson Forecast. June 2007. A ccessible with subscription as of October 21, 2008 at: http://www.uclaforecast.com/forecast/forecast.asp?s ForecastType=ArticleG-92

United States Census Bureau Service Sector Statisti cs Division, Transportation Characteristics Branch, 2002. Vehicle Inventory an d Use Survey – Discontinued Products. Available at: http://www.census.gov/svsd/www/vius/products.html

United States Department of Transportation, Federal Highway Administration, 2007. Highway Statistics 2006, Washington, DC, 2007. Ava ilable at: http://www.fhwa.dot.gov/policy/ohim/hs06/index.htm

United States Environmental Protection Agency, 2008 . Emission Facts: Average Carbon Dioxide Emissions Resulting from Gasoline an d Diesel Fuel. In: Overview: Pollutants and Programs. Available at: http://www.epa.gov/oms/climate/420f05001.htm