1 

### ARB Staff Assessment of the Impact of the Economy on California Trucking Activity and Emissions 2006-2014

California Air Resources Board
December 2009

Summary

At its December 2008 meeting, the Calif ornia Air Resources Board (ARB or Board) adopted the Statewide Truck and Bus Rule (Rule). At that Hearing, the Board directed ARB staff to report back in December 2009 on the impact of the current recession on heavy duty diesel truck activity and emissions in California.
This document describes the data sour ces and approach taken to evaluate near term statewide impacts of the recession on trucking activity and emissions as
well as the results of a sensitivity analysis assessing the implications of two different growth scenarios for statewid e truck and bus emissions through 2014. With the exception of drayage trucks, t here was insufficient information available to accurately characterize how the rece ssion is impacting truck emissions either regionally or by type of trucking vocation.

In conducting this assessment, staff anal yzed a wide variety of data sources including diesel fuel sales, roadway sensor information, shipping container throughput, trucking industry tonnage repor ts, truck sales trends, and truck registration data. Based on the data reviewed diesel fuel use, truck traffic, and a
number of other state wide surrogates of truck activity in California decreased by approximately 20 percent between 2007 and 2009. The lower emissions associated with this reduced activity has been partially offset by the overall age of the California-register ed truck fleet increasing by approximately one year between 2006 and 2009 due to a 60 percent r eduction in new truck registrations in California over that time period. Combined, the reduced truck activity and older average fleet age results in calend ar year 2009 emissions being 20 percent
lower than was estimated in t he Rule staff report.

Looking forward, there is no si ngle metric or forecast that is a direct indicator for projecting future emissions fr om trucks and buses in Califor nia. As a result, staff reviewed a large number of economic and fuel consumption forecasts for
California and the nation. These incl uded forecasts from national agencies such as the Congressional Budget Office and the Energy Information Administration; California state agencies including t he Department of Finance, Legislative Analyst’s Office, and California Ener gy Commission; leading California universities including the UCLA Anders on School and the University of the Pacific; and independent consult ants such as Beacon Economics.

These forecasts provided a range of differe nt rates of economic recovery. To encompass the variability in these forecasts staff conducted a sensitivity analysis of potential future emissions trends . The upper and lower bounds of this2 sensitivity analysis were defined by two growth rates and informed by the economic, fuel, and other forecasts review ed. Staff defined a faster growth
scenario where California truck activi ty and sales begin to grow in 2010 and return to the long te rm trend in eight years, and a slower growth scenario in which California truck activity and sale s are flat from 2009 to 2011 and then grow at historically average rates through 2014. The eight year recovery period assumed in the faster grow th scenario is consistent with the length of time required for the U.S. economy to recover fr om the Great Depre ssion. It is also supported by the Congressional Budget Office forecast coupled with an assumption that California’s economic re covery will lag the nation by two years. The rate of growth assumed in the slower growth scenario is generally consistent
with most of the Califor nia-specific employment forecasts reviewed.

Results show that in the near term (2010 and 2011), in both scenarios evaluated, lower truck activity will reduce diesel particulate matter (PM 2.5 ) and oxide of nitrogen (NOx) emissions to a greater degree than was expected in the 2008 Rule staff report. However, beginning in 2012 and continuing through 2014, the Rule is required to achieve needed emissions reductions for both growth
scenarios considered. Under a faster growth scenario, th e Rule in its entirety Is needed to meet State Implem entation Plan (SIP) obligations for NOx and PM 2.5

. Under a slower growth scenario, the Rule provides slightly more emissions reductions than were expected in the Rule staff report. This analysis shows that assumptions about the rate of economic recovery and related growth in truck activity and sales determines whether t he Rule provides any NOx emissions reductions above and beyond what was ex pected in the Rule staff report.

Introduction

In December 2008 the California Air Resources Board (ARB) adopted the Statewide Truck and Bus Rule (Rule). At that hearing, the Board directed ARB staff to report back to t he Board in December 2009 on the impact of the current recession on heavy duty diesel vehicle activi ty and emissions in California. This document describes the data sources and approach taken to evaluate near term impacts of the recession as well as the re sults of a sensitivity analysis assessing the implications of different economic scenarios for California truck emissions through 2014.

In developing the Rule in 2008, ARB staff updated assumptions about the population of trucks operating in California, the annual miles traveled by different trucking vocations, their average age, and sale s. A complete description of these sources and how they were used to estimate emissions for the Rule is provided in Appendix G of the Rule staff repor t. This assessment uses the same
emissions estimation methods as were us ed for the staff report but incorporates more current truck activity, population, and age data from a variety of sources.3 Assessing Current Truck Activity

Staff evaluated a variety of different data sources to assess the current impact of the recession on trucking activity.

 On-Road Diesel Fuel Sales – the California State Board of Equalization (BOE) collects and makes publically av ailable California-specific on-road diesel fuel sales estimates on a monthly basis. There is a 4 month lag in the
release of the BOE fuel sales data such that the most recent fuel sales data used in this analysis was August 2009; data were evaluated back to 2000. Fuel sales vary seasonally; the magnitude of the calculated reduction in fuel sales can vary depending upon the time period selected for analysis.

 Highway Performance Monitoring System (PeMS) – Thousands of roadway sensors are embedded on freeways and state highways throughout California. This system was develope d by the California Department of Transportation (Caltrans) to assist traffic management efforts and summary reports are published through Caltrans and UC Berkeley. Truck volume data are estimated on an hourly basis at each site, allowing trends in truck activity to be developed, but this is complicated by the fact that sensors are added, removed, and repaired every year.

PeMS data are most useful when high quality data are available for several consecutive years at the same site or set of sites. Staff reviewed data for eight
PeMS stations as shown in Table 1 that were selected based on their location on major truck thoroughfares and their data quality over the period 2004 - 2009.
Staff also evaluated California and r egional summary data provided by UC Berkeley through their PeMS web site. These data are available in near-real time; the most recent PeMS data used in this analysis were from October 2009.

Table 1. Selected PeMS Locations Location Freeway Station No. County Norwalk I5 Northbound 715915 Los Angeles

Oakland I580 Westbound 400549 Alameda

Ontario I10 Eastbound 801269 San Bernardino Los Angeles SR101 764781 Los Angeles San Joaquin Valley and French Camp Road SR99 Northbound 1004910 San Joaquin

Irvine I405 Northbound 1201333 Orange East of Pleasant Valley I80 Westbound 401877 Solano

San Jose I880 Southbound 400514 Santa Clara

 Weigh-in Motion (WIM) Stations – More than one hundred weigh-in motion sensors are embedded on freeways and state highways in California. These sensors provide very detailed and a ccurate information on truck volumes,4 weights, axle configuration, and speeds throughout the State. Staff obtained multi-year historical data from Calt rans for nine selected sites and analyzed
the results. These sites were chosen based on their location and data completeness between 2004 and 2009. Selected locations are shown in Table 2. The WIM data require signifi cant processing such that the most recent data used in this analysis were from March 2009.

Table 2. Selected WIM Stations

Location Freeway Station
No. County

Fresno SR-99 SB 10 Fresno Antelope I80 (East of Sacramento) 3 Sacramento Hayward I880 NB 18 Alameda

Fontana I15 NB 70 San Bernardino Balboa I15 NB 88 San Diego

Chico SR-99 SB 107 Butte Ports of LA / Long Beach I710 Near I405 Interchange 116 Los Angeles Carbona 580 WB in San Joaquin County 113 San Joaquin

Willows I5 NB 108 Glenn

 Port Container Throughput – The Ports of Los Angeles, Long Beach, and Oakland publish historical and current port-specific container throughput statistics in a monthly database. The most recent container throughput data analyzed for this assessment was fr om October 2009; data were evaluated
back to 1995.

 American Trucking Associat ion (ATA) Truck Tonnage Index – Each month the ATA asks its national membership the amount of tonnage each carrier hauled, including all types of freight. These data are summarized, normalized to the 2000 calendar year, and published. The index represents the relative activity of ATA members, and is not spec ific to California. The most recent ATA truck tonnage data analyzed for this assessment was from September 2009; data were analyzed back to 2005.

 Bureau of Transportation Statistics (BTS) Transportation Services Index – The TSI is a seasonally adjusted, we ighted measure of the movement of freight which is published monthly. T he index is national and not specific to California. The most recent BTS transportation services data analyzed for this assessment was from September 2009; data were evaluated back to
1990.5 A complete listing of each source and the estimated decrease in activity relative to calendar year 2007 is provided in Table 3.

Table 3. Estimated Decrease in Current Activity Relative to 2007

Data Source Region Change 2007-2009 Latest Data Used California Fuel Sales Statewide -13% to -18% August 2009
California PeMS Counts Statewide -4% or more October 2009
California WIM Counts Statewide -10% or more March 2009 Port of LA / Long Beach
Container Traffic Los Angeles -26% October 2009

Port of Oakland
Container Traffic Bay Area -17% October 2009

ATA Tonnage Index Nationwide -10% September 2009 BTS Transportation
Services Index Nationwide -14% September 2009

Results show that truck activity in Ca lifornia is down between 4% and 26% since 2007, depending on the data source. Staff be lieves statewide fuel sales is the most appropriate measure of the impact of the recession on non-drayage trucks,
which is consistent with national trends. Preliminary analysis of PeMS and WIM data show significant variability in tru ck activity reductions depending upon the
location and staff are continuing to assess regional differences.

Staff believes there are significant differ ences in the impact of the recession on different types of trucking operations. For example, as shown in Table 1, container movements through California ’s ports and associated drayage truck activity have been impacted more signifi cantly than trucking operations in general. Also, according to the Califor nia Department of Finance, construction employment has decreased significantly mo re than transportation employment as a whole. However, with the exception of drayage trucks, staff was not able to identify trucking activity-specific statis tics for individual trucking sectors in
California.

Assessing Current Truck Age in California In developing the emissions analysis docum ented in the Rule staff report, staff evaluated the impact that trucking sales hav e on the age distribution of vehicles,
and found that national sales statistics are correlated wit h truck age distributions in California. This is important bec ause newly manufactured trucks are cleaner
than older trucks. This correlation wa s used as an input to the emissions analysis, so that historical and future fo recasted truck sales have an impact on the modeled age distribution of vehicles in each calendar year. When more vehicles are sold nationally, more appear in California fleets; and when fewer vehicles are sold nationally, fewer appear in California fleets. To validate this assumption, staff analyzed trends in nat ional new truck sales reported by WardsAuto and changes in the fraction of new truck registrations reported by the California Department of Mo tor Vehicles (DMV).6 WardsAuto is a company that monitors and publishes national truck sales data that are available through a subscription se rvice. Figure 1 shows historical new truck sales, and indicates that current sale s volumes are at their lowest level in 25 years. The table also shows significant year to year variability and sensitivity
to economic conditions.

Figure 1. National Heavy Duty Diesel New Truck Sales 1985-2009

0 50 100 150 200 250 300

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009Thousands of Trucks

Calendar Year
14,001- 33,000 lbs 33,001 lbs. & over 2006 1999

2003 1995

1991

Because national truck sales are so low, staff expects that the average age of vehicles operating in Calif ornia is getting older. St aff assumes the relationship
between vehicle sales and r egistered truck population applies to all trucks regardless of registered location, body ty pe, or vocation. This assumption has
been confirmed for Californi a registered trucks by anal ysis of California DMV registration data, as described below.

Every six months ARB staf f obtains a snapshot copy of the DMV registration database. The database contains roughly 50 million records that staff process to count the number of cars and trucks by we ight class, technology, age, and other factors. Staff processed the database to count California-registered non-drayage heavy-heavy duty diesel trucks by model year. Drayage trucks were removed in order to focus this analysis on vehicle sales caused by normal business vehicle replacement decisions, and not by replac ement decisions driven by compliance requirements. Staff identif ied roughly 3000 new vehicle r egistrations in 2009 that were also registered in the ARB dray age truck registry. These new vehicle registrations in the drayage fleet were higher than normal new vehicle7 registration rates for drayage trucks, suggesting that these additional registrations were completed to comply with Drayage Truck Rule requirements effective January 2010, and were not caused by natural turnover. The emissions reductions generated by compliance with t he Drayage Truck Rule are attributed
to that Rule and not to the St atewide Truck and Bus Rule.

Table 4 shows that between 2005 and 2007, approximately 6% of all registered in-state non-drayage heavy-heavy duty dies el trucks were new model year
vehicles. The associated 10% increase in truck population over that same time period suggests that in those years the number of new trucks entering the
California fleet exceeded the number of truc ks being retired. However, starting in 2008, new truck registrations dropped dramatic ally, consistent with the decline in national truck sales, such that le ss than 3% in-state non-drayage trucks registered in 2009 were new model year vehicles. This drop in new vehicle registrations resulted in the truck popul ation leveling off and the average fleet age increasing by almost a year as fe wer new vehicles were purchased and existing vehicles stayed on the road longer.

Table 4. Estimated DMV Registration Da tabase Heavy-Heavy Duty Registered Non-Drayage Truck Populati on in California: 2005-2009
Year Percent of
New
Vehicles in
Fleet Population (thousands)(nondrayage) Average Age

2005 6.4% 181 9.1
2006 6.8% 198 9.1
2007 6.1% 199 9.2
2008 3.4% 198 9.6
2009 2.7% 197 9.9

Table 5 compares the reduction in nat ional new truck sales (as measured through national sales data) to the reduction in the num ber of new non-drayage registered vehicles as m easured through the California DMV database. Results show comparable reductions between 2005 and 2009 in new truck sales on a nationwide basis and registration of new model year non-drayage trucks in
California.8 Table 5. Trend in National New Tru ck Sales and New Registered Non-Drayage
Trucks in California: 2006-2009 % Change Relative to 2005
Year National 1 California 2

2006 +12% +6%
2007 -40% -5%
2008 -46% -47%
2009 -64% -58%*

1 WardsAuto national sales database

2 Analysis of California DMV new truck registration data * Excludes drayage trucks

The Emissions Analysis

The Rule emissions analysis reflects annual average long term growth and did not evaluate emissions under the cyclic al conditions of periodic economic expansion and contraction. However, the Rule emissions analysis has two important inputs that can be used to reflect the econom ic cycle: activity growth rates, and sales forecasts.

During a recession the economy contracts du e to less economic activity. In the trucking sector this translates into fewer overall miles driven and fewer average miles driven per truck. Some trucking firms may operate fewer vehicles during a recession than during stable ec onomic periods. The net effect is that during a recession (assuming no changes in average v ehicle age), fewer miles are driven,
and emissions are reduced as a result.

During stable economic periods trucking firms will operate their vehicles and as mileage accumulates, vehicles will eventual ly reach the end of their economic or
useful life. When this occurs firms will replace older trucks with new or newer
used vehicles. This turnover of vehicl es is a standard practice in the industry during stable economic periods and varies by type of business. However, during
a recession this turnover is affected in two ways. First, because fewer miles are being driven per truck per year, older vehi cles will last longer than they otherwise would have, reducing the demand for new or newer used vehicles. Second, because businesses are more financially stressed during a recession they are less likely or able to purchase new or newer used vehicles. The combined impact of these two factors is that over time the fleet gets older. Because older
vehicles have fewer and less effective em issions controls than newer vehicles, fleet average emission rates will increase over time.

During this recession, the decreased demand for newer vehicles has resulted in
lower vehicle sales. As shown in Figure 1 and Table 5, 2008 and 2009 model year sales and registrations have been very lo w relative to historical values. In9 the absence of regulatory r equirements, 2008 and 2009 m odel year vehicles will
enter the California fleet both now and in t he future in numbers significantly lower than previously assumed. Because 2007 standard engines are 70% cleaner for
NOx and 85% cleaner for PM 2.5 than engines manufactur ed prior to 2007, fleet average emission rates will be higher both now and in the future relative to what was anticipated in the Rule inventory.

Revised 2009 calendar year emissions es timates were developed using the most recent diesel fuel sales, truck activity, and truck sales data available. A listing of the fuel sales and activity data sources used are shown in Table 3 and the truck sales estimates used are provided in Tables 4 and 5. Figures 2 and 3 show that
these revised PM 2.5 and NOx emissions estimates for calendar year 2009 are approximately 20% lower than the estimates provided in the Rule staff report.

Figure 2. 2009 PM 2.5 Emissions: 2008 Staff Repor t vs. Revised Estimate

0 5 10 15 20 25 30 35

Staff Report Revised AnalysisPM2.5(Tons per Day)10 Figure 3. 2009 NOx Emissions: 2008 St aff Report vs. Revised Estimate

0 100 200 300 400 500 600 700 800 900

Staff Report Revised AnalysisNOx (Tons per Day)

The Statewide Truck and Bus Rule phases -in compliance requirements for NOx
and PM 2.5 over a thirteen year period starting in 2011. The first round of requirements take e ffect over three years beginni ng in 2011, and requires nearly all vehicles to be equipped with a diesel particulate filter by 2014. These relatively near-term requirements are fo cused on reducing near-source risk in the vicinity of ports, heavily traveled roadways, and environmental justice communities; and on providing emissions r eductions in 2014 to meet SIP targets.

The second round of requirements in the Rule focuses on reducing NOx emissions to meet both particulate matter and ozone air quality standards. Requirements are phased in between 2012 and 2023 and are designed to meet two objectives. The first objective is to meet regional SIP targets in the South Coast, San Joaquin, and other regions of the state. The second is to ensure that by 2023 all trucks operating in California will be equipped with the lowest NOx
and PM 2.5 emissions control technology available.

Because the Rule is designed to phase- in compliance over time, understanding how the recession will impact future year emissions is important. The Rule emissions analysis documented in the staff report assumed long-term annual average growth in activity and an estimate of future new vehicle sales. In order to assess the impact of the recession, staff evaluated ec onomic and fuel consumption projections from a wide variety of sources in order to estimate how activity and new vehicle sales will c hange during and after the recession.11 Forecasts Used Staff evaluated a number of economic, fuel , and other forecasts at the local, state, and national leve l including:

Economic Forecasts

 UCLA Anderson School Since March 2006 the UCLA Anderson Forecast has provided quarterly
economic forecasts for California and the U.S. The most recent forecast, published in September 2009, provides forecasts for employment, gross domestic product (GDP) and housing permits out to 2011. Previous publications (e.g. 2007 and 2008) have pr ovided extended fo recasts out to 2020, but the 2009 forecast did not do so.

 University of the Pacific
The California and Metro Forecast is a quarterly forecast of the economies of California and 11 metropolitan areas in northern California (Modesto, Merced, Napa, Fresno, Oakland, Sacramento, San Francisco, San Jose, StocktonLodi, and Vallejo-Fairfield). Published in September of this year the report provides forecasts for gross state product, employment, housing permits, and population out to 2014.

 Beacon Economics Beaconomics is a quarterly economic fore cast for the U.S. and California. Published in September of th is year the report provides forecasts for personal income, employment, housing permits, and imports/exports out to 2013.

 California Department of Finance The forecasts were prepared by the Econom ic Research Unit of the California Department of Finance in April 2009. The reports provide annual and quarterly economic forecasts employm ent, income, and housing permits for
California and the U.S. out to 2011.

 California Legislative Analyst’s Office The LAO’s “2010-11 Budget: California’s Fiscal Outlook” published in November 2009 provides economic projecti ons on California’s budget as well
as California and U.S. em ployment, GDP and housing permits out to 2015.

 United States Congres sional Budget Office In the CBO’s “The Budget and Economic Outlook: An Update” published in August 2009 they provide a forecast of U.S. real and potent ial gross domestic12 product out to 2019. The projections suggest that real GDP will converge with potential GDP no later than 2015.

Fuel Consumption and Other Forecasts

 California Energy Commission
CEC staff for the 2009 Integrated Energy Policy Report , developed long-term forecasts of transportation fuel demand. The Fuel Demand Forecast provides diesel fuel consumption fo r four primary areas: truck and rail freight goods movement, residential and commercial li ght-duty vehicle transportation, urban and intercity public transit, and off-road vehicles. The report was published in August 2009 and forecasts fuel demand out to 2030.

 United States Energy Information Administration The Annual Energy Outlook 2009 published in March 2009 provides projections of US energy supply, demand, and prices through 2030. The projections are based on results from t he Energy Information Administration's National Energy Modeling S ystem. The reference case was updated in April 2009 to reflect the American Recovery and Reinvestment Act adopted in February 2009. The Annual Energy Outlook forecasts national fuel use, truck
sales and truck vehicle miles travel ed through 2030 but does not include any information specific to California.

Table 6 compares each of the forecasts in terms of the information provided, when the forecast is last published, and when the next release is anticipated.

Projecting heavy duty truck emissions into the future requires developing growth rates that represent Californi a-specific estimated vehicle activity (vehicle miles traveled or VMT) and new vehicle sales in each future year. These growth rates can then be input to the emissions analysis to calculate future year emissions.

Staff evaluated each forecast, and found t hat none provided California-specific growth estimates for truck VMT or tru ck sales in future years. Staff then evaluated each forecast to compare inform ation provided across forecasts. Both the Annual Energy Outlook (AEO) from the United States Energy Information Administration and the Integrated Energy Policy Report from the California Energy Commission provided future year fu el consumption forecasts. The AEO also provided truck VMT and truck sales forecasts, but these were nationwide projections and did not provide any esti mates for California specifically.13Table 6. Comparison of Economicand Fuel Consumption ForecastsEconomic ForecastsRegion Data Source Information Provided Forecast to Date Last Published Future Release DateCalifornia UCLA AndersonSchoolCA non-farm employment bysector; California and nationalGDP; housing permits.2011 September 2009 December 2009Univ. of the Pacific CA non-farm employment bysector; California GDP; housingstarts. Forecasts statewide andregional.2014 September 2009 December 2009Beacon Economics CA non-farm employment bysector; California and nationalGDP; housing permits; andimports/exports.2013 September 2009 December 2009CA Dept of Finance CA non-farm employment bysector; California and nationalGDP; housing permits.2011 April 2009 2010CA Office of theLegislative AnalystCA non-farm employment bysector; California and nationalGDP; housing permits.2015 November 2009 2010United States CongressionalBudget Office (CBO)National Real GDP 2019 August 2009 Early 2010Fuel Consumption ForecastsRegion Data Source Information Provided Forecast to Date Last Published Future Release DateCalifornia CEC Fuel DemandForecastCA diesel fuel demand 2030 August 2009 August 2010United States Energy InformationAdministration (EIA)National truck VMT and newtruck sales2030 April 2009 201114 Most California-specific economic fore casts evaluated by staff provided a statewide assessment of pr ojected economic measures such as gross state domestic product and employment. Those economic measures are not direct indicators of trucking activity or vehi cle sales but are useful for assessing the potential timing and rate of a future econo mic recovery. Most of the available
California forecasts focused on the near-t erm (the next three to five years) and did not forecast longer term trends. Fi nally, each economic forecast projected different recovery rates for economic indicators such as gross domestic product and employment.

Bounding Potential Emissions Forecast Scenarios As part of this assessment, staff devel oped a bounding exercise to bracket the range of potential economic recovery conditions and their impact on heavy duty
vehicle emissions in California. Ba sed on the range of economic and other forecasts discussed previously, staff developed a faster growth scenario and a slower growth scenario for the y ears 2010 through 2014. These faster and slower growth scenarios bracket the r ange of available forecasts that were reviewed. Both scenarios provide em issions projections for several broad trucking categories: non-drayage heavy-heavy duty trucks and buses (>33,000 lbs gross vehicle weight rating); non- drayage medium-heavy duty trucks and buses (14,000-33,000 lbs gro ss vehicle weight rating), drayage trucks at the Port of Los Angeles and Long Beach, and drayage trucks at the Port of Oakland.

The faster growth scenario assumes t hat by 2017 trucking activity and vehicle
sales in California will return to levels anticipated in the Rule staff report. The return to anticipated activity and vehicle sales levels by 2017 is based on historic recovery rates from previous recessions and also accounts for the idea that California is likely to recover more slo wly from this recession than the country as a whole. Specifically, t he Congressional Budget Office historical national gross domestic product data indicates that no nat ional recession or depression since 1929, including the Great Depression, has taken more than eight years to recover back to previously anticipated growth levels. In addition, the Congressional Budget Office forecast s uggests that real gross domestic product at a nationwide level will converge with potential gross domestic product trends no later than 2015. Coupling this national forecast with the assumption that California’s recovery will lag the nation by several years yields the 2017 recovery date assumed for the faster growth scenario.

For this analysis, the vehicle activity recovery rate was assumed to be linear between current 2009 levels and anticipat ed 2017 levels, which results in a recovery rate of approximately 6.3% per year in heavy heavy-duty diesel truck activity. Because vehicle sales are much more sensitive to changes in economic conditions than is truck activity, the gr owth in heavy heavy-duty diesel vehicle15 sales was assumed to be 36% per year for 2010 and 2011, and 4.8% per year
from 2012 to 2017.

The slower growth scenario is based on the assumption that trucking activity and new vehicle sales will not return to pr eviously anticipated levels in the foreseeable future. Staff assumed that truck activity will remain flat in 2010 at 2009 levels, and then grow at annual average growth rates reflected in long term trends and published in the staff report (whi ch as documented in the staff report is 2.6% per year for heavy heavy-duty dies el trucks). This scenario is generally
consistent with the economic outlook def ined by the California Legislative Analyst’s Office (November 2009), nati onal trends in the AEO, and recovery rates in most California-specific empl oyment forecasts. Vehicle sales are assumed to be flat in 2010 and 2011, with 11% per year growth occurring
between 2011 and 2017.

Method for Forecasting Emissions Staff developed a scaling approach for es timating the impacts of projected reduced activity and projected reduced vehicle sales on emissions for each calendar year between 2010 and 2014. The scaling approach starts with the emissions estimated by the Rule analysis, and ratios those emissions to reflect the change in activity, sales, and the r eductions resulting from the Rule. The scaling approach is conducted in a Micr osoft Access database. The following equation was used to calculat e emissions for NOx and PM 2.5 without the application of reductions generated by the Rule:



# Σ E sypc = E bypc x A syc x S syc (1)

where

E sypc = Emissions after rule is applied for scenario “s,” year “y,” pollutant “p,” and category “c”
E bypc = Baseline emissions from rule inventory analysis for year “y” and pollutant “p,” and category “c”
A syc = Activity ratio reduction factor for scenario “s,” year “y,” and category “c”
S syc = Sales ratio reduction factor for scenario “s,” year “y,” and category “c”

The following equation was used to ca lculate emissions for NOx and PM 2.5 (prior to 2014) after the Rule is applied:



# Σ E sypc = E bypc x A syc x S syc x R ypc (2)
where,

R ypc = Rule ratio reduction factor for scenario year “y,” pollutant “p,” and category “c”

A similar scaling equation was used for calculating PM 2.5 emissions after application of the Rule in 2014 except that the sales r eduction ratio is not used16 because after the implementat ion of the Rule for PM 2.5 in 2014, all vehicles are required to be equipped with a diesel particu late filter. As a result, the increase in age does not materially affe ct remaining emissions after the Rule is applied.

The equation for PM2.5 emissions in 2014 is as follows:



# Σ E sypc = E bypc x A syc x R ypc (3)

In these equations, the activity ratio r eduction is calculated as the projected activity level (vehicle miles traveled) divided by the anticipated activity level in the Rule inventory analysis (vehicle miles tr aveled) in each calendar year for each
scenario.

The rule reduction factor is calculated as the ratio of the Rule emissions analysis with the rule applied in each calendar year divided by the Rule emissions analysis baseline inventory wit hout the rule applied. This ratio is calculated for each calendar year, pollutant and inventory category in th e Rule inventory, and is applied independent of economic scenario. The fact that the ratio is applied independent of economic scenario is a si mplification because the model year
distribution of vehicles wil l vary from the baseline in each economic scenario due
to different forecasts of vehicle sales and their impac t on projected age distributions in each category and fleet size group.

The sales reduction ratio is calculated us ing a two step proce ss that adjusts the fraction of a model year in an age distri bution by the ratio of projected sales under a bounding scenario to annual average projected sales. This ratio reflects the fact that a recession reduces the orig inally estimated new vehicle sales and vehicle turnover, and as a result the vehi cle fleet becomes older. In the Rule inventory analysis vehicle turnover is a f unction of national new vehicle sales, the methodology for which is described on page s 46-53 of Appendix G of the Staff Report for the Statewide Truck and Bus Rule. In the methodology, the fraction of each model year in the inventory categor y age distribution is a function of an average age distribution and a sales fore cast, as shown in equation 4. This fraction for each model year and calendar year is normalized to one, as shown in equation 5.
F MY,CY,c = A MY,CY,c x (S s / S a ) (4) where,
F MY, CY, c = Fraction of a given model year in a calendar year and inventory category.
A MY, CY, c = Average fraction of a given m odel year in a calendar year and inventory category, calculat ed using 2000-2005 historical
DMV data.
S s = Projected sales in a forecast scenario “s.”
S a = Projected average annual sales, calculated with by regression line through historical tru ck sales 1985-2009 and projected
into the future.17 Fc c = (F MY,CY,c x 100) / 

## Σ F MY,CY,c (5) where,
Fc c = Corrected fraction of a given model year in a calendar year and inventory category.

Using this methodology, the fraction of a model year in a calendar year and inventory category can be adjusted using di fferent sales forecast scenarios. To assess the impact of this assumption, the VMT-weighed av erage emission rate can be compared across economic scenar ios. As an example, Table 7 compares the heavy-heavy duty diesel Calif ornia registered in-s tate tractor fleet average PM 2.5 and NOx emission rate for t he 2008 Rule emissions analysis published in the staff report, the faster growth scenario , and the slower growth scenario in calendar year 2014. The table shows that the expected fleet average emission rate in 2014 is significantly hi gher than assumed in the staff report due to sharply lower truck sales. Depending upon the rate of truck activity growth, fleet average emission rates in 2014 will be 6% to 16% higher for NOx and 8% to 21% higher for PM2.5 than was assu med in the 2008 Rule analysis.

Table 7. Heavy-Heavy Duty Diesel Tractor VMT Weighted Emission Rates (grams per mile) in 2014 by Growth Scenario.

Scenario PM 2.5 Percent NOx Percent 2008 Rule Emissions Analysis 0.49 100% 10.2 100%
Faster Growth Scenario 0.52 108% 10.8 106%
Slower Growth Scenario 0.58 121% 11.9 117%

Results Figure 4 compares estimated PM 2.5 emissions resulting fr om implementation of
the Rule as described in the 2008 staff report to what w ould happen under the two growth scenarios assumed. Because t he Rule is phased-in over a number of years, most of the Rule reductions are expected to occur between 2012 and 2014. The figure shows in 2011 the recession provides more emissions reductions than the Rule was projected to provide in the Rule staff report. After 2011 the Rule is projected to provi de more emissions reductions than the recession alone would provide. After 2012 th e Rule provides significantly more
emissions reductions than under either the fa ster or the slower growth scenarios.18 Figure 4. Statewide PM 2.5 Emissions 2011-2014

0 5 10 15 20 25 30

2011 2012 2013 2014
Calendar YearPM2.5Tons per Day Faster Growth Without Regulation Slower Growth Without Regulation Staff Report Projection With Regulation

Due to the phase-in of requirements in the Rule, NOx emissions reductions are not expected until 2013. Fi gure 5 compares NOx emissi ons reductions projected to be achieved by the Rule in the 2008 st aff report to the faster and slower growth scenarios. Results show that in 2013 and 2014 the Rule is anticipated to provide more emissions reductions t han would be achieved by the recession
alone.

Figure 5. Statewi de NOx Emissions 2011-2014

0 100 200 300 400 500 600

2013 2014
Calendar YearNOx Tons per Day Faster Growth Without Regulation Slower Growth Without Regulation Staff Report Projection With Regulation19 The Statewide Truck and Bus Rule was designed to provide sufficient NOx and
PM 2.5 emissions reductions to meet SIP ta rgets in the South Coast region in 2014. The next step in the analysis wa s to estimate calendar year 2014
emissions under the faster and slower growth scenarios and compare the results to the SIP target. Figure 6 compares 2014 PM 2.5 emissions in the faster and slower growth scenarios after the Rule is applied to anticipat ed emissions levels in the staff report and SIP target. Results suggest that in the faster growth scenario, the Rule is not projected to provide significantly more PM 2.5 emissions reductions than anticipated in the staff report. In t he slower growth scenario, emissions are slightly lower than ant icipated in the st aff report.

Figure 6. Projec ted 2014 Statewide PM 2.5 Emissions (tons per day)

0 5 10 15 20 25

Staff Report Faster Growth Slower GrowthPM2.5Tons per Day

Without Rule With Rule Reduction level to meet SIP target

Figure 7 compares 2014 NOx emissions in the faster and slower growth scenarios after the Rule is applied to ant icipated emissions levels in the staff report. Under the faster growth scenario the drop in truck activity that reduces emissions is offset by the increased emissions caused by decreased vehicle
sales and fleet turnover. Under the slow er growth scenario the impact of the drop in activity due to the recession reduc es emissions more than the impact of
reduced sales increases emissions. Afte r the Rule is applied to the slower growth scenario, the Rule might provide sl ightly more emissions reductions than were anticipated in the Rule staff report. This analysis shows that assumptions about the rate of economic recovery and related growth in truck activity and sales determines whether the Rule provides any NOx emissions reductions above and beyond what was expected in the Rule staff report.20 Figure 7. Project ed 2014 Statewide NOx Emissions (tons per day)

0 100 200 300 400 500 600

Staff Report Faster Growth Slower GrowthNOx Tons per Day

Without Rule With Rule Reduction level to meet SIP target

Conclusions The economy has had a significant impact on emissions from trucks and buses in
California. Staff estimates emissions in 2009 have been reduced by more than 20% relative to estimates published in the 2008 staff report. Projected future emissions are dependent upon how Californ ia truck activity and truck sales
recovers from the recession over the next five years. Staff developed two scenarios that were designed to bound the range of economic and fuel forecasts that were reviewed. Results suggest the recession will reduce emissions in 2011 more than was anticipated by the Rule in the staff report because the Rule is phased-in and relatively few reductions we re anticipated in 2011. After 2012 the Rule is expected to generate much lower emissions than would be generated by the recession alone. In 2014, the analysis demonstrates the Rule is necessary to achieve the SIP target, and the extent to which the economy recovers will dictate
whether or not the Rule will generate any emissions reductions above and beyond what was expected in the staff report.21 References Beacon Economics (2009). Beaconomics, September 2009. Available at: http://www.beaconecon.com/Beaconomics/

California Air Resources Board (2008). Staff Report: Init ial Statement of Reasons for Proposed Rulemaking – Proposed Regulation for In-Use OnRoad Vehicles: Appendix G: Emissions Inventory Methodology and Results. Available at: http://www.arb.ca.gov/regact/2008/truckbus08/appg.pdf

. California Air Resources Board (2008). Staff Report: Init ial Statement of Reasons for Proposed Rulemaking – Proposed Regulation for In-Use OnRoad Vehicles: Emission Invent ory Data. Available at: http://www.arb.ca.gov/regact/ 2008/truckbus08/emissinv.xls

. California Department of Finance (2009). Latest California Economic Data.
Available at: http://www.dof.ca.gov/HTML/FS_DAT A/LatestEconData/Data_home.htm

. California Department of Finance, (2009). Economic Forecasts, US and
California. Available at: http://www.dof.ca.gov/HTML/FS_DATA/ LatestEconData/FS_Forecasts.htm

. California Department of Transportati on (2009). Data Weigh in Motion. Available at: http://www.dot.c a.gov/hq/traffops/trucks/datawim/

. California Energy Commission (2009). Transportation Energy Forecasts and Analyses for the 2009 Integrated Energy Policy Report. Available at: http://www.energy.ca.gov/publicat ions/searchReports.php?div_num=600

. California Legislative Analyst’s Office (2009). California’s Fiscal Outlook: The 2010 – 2011 Budget. Available at: http://www.lao.ca.gov/laoapp/ PubDetails.aspx?id=2143

. California State Board of Equalization, ( 2009). Fuel Taxes Division Statistics and Reports. Available at: http:// www.boe.ca.gov/sptaxpr og/spftrpts.htm

. Port of Long Beach (2009). Port Statistics. Available at: http://www.polb.com/about/ port_stats/default.asp

. Port of Los Angeles (2009). TEU Statistics (Container Counts). Available at: http://www.portoflosangeles .org/maritime/stats.asp

. Port of Oakland (2009). Container Statistics. Available at: http://www.portofoakland.com/ maritime/facts_cargo.asp

. UCLA Anderson (2009). UCLA Anderson Sc hool Forecast for the Nation and California, September 2009 Report. Available by subscription at: http://www.uclaforecast.com/forecas t/forecast.asp?sForecastType=Article

United States Bureau of Transportati on Statistics (2009). Transportation
Services Index. Available at: http://www.bts.gov/xml/tsi/src/index.xml

. United States Congressional Budget Of fice (2009). The Budget and Economic Outlook, An Update. Available at:22 http://www.cbo.gov/ftpdocs/105 xx/doc10521/2009BudgetUpdate_Summary.p
df

and http://www.cbo.gov/ft pdocs/105xx/doc10521/Table2-2- SupplementalInformation.xls and http://www.cbo.gov/publicat ions/bysubject.cfm?cat=0

. United States Energy Information Administ ration (2009). Annual Energy Outlook, 2009. Available at: http:// www.eia.doe.gov/oiaf/aeo/index.html

. University of California at Berkeley, (2009). Freeway Perf ormance Measurement System (PeMS 10.2). Available at: https://pems.eecs.berkeley.edu/?r edirect=%2F%3Fdnode%3DState

. University of the Pacific Eberhardt Sc hool of Business, Business Forecasting Center (2009). California and Metro Fore cast, September 2009. Available by subscription at: http://forecast.pacif ic.edu/CAmetro.html

WardsAuto.com (2009). U.S. Sales of Domestic and Imported Trucks. Available by subscription at: http://subscribers.wardsauto.com/refcent er/northamerica/us/yearly/sales/gvw/
UsaSa20.xls ; and http://subscribers.wardsauto.com/re fcenter/northamerica/us/monthly/

.