California Electric Transportation
Return on Investment Assessment
FINAL REPORT
Submitted to
Submitted by
Point of Contact:
Michael F. Lawrence, JFA President
4550 Montgomery Avenue Suite 300N
Bethesda, Maryland 20814
Phone: (301) 961-8835
Fax: (301) 469-3001
lawrence@jfaucett.com
June 2015
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Table of Contents
Executive Summary ........................................................................................................................ 1
1.
Introduction ............................................................................................................................. 2
1.1 Background on EV Incentives for California Purchasers ..................................................... 2
1.2 Objectives of the Analysis .................................................................................................... 5
1.3 Previous ETC Research by U.C. Berkeley ........................................................................... 7
1.4 Organization of the Report.................................................................................................... 8
2.
New Vehicle Elasticity Estimates ......................................................................................... 10
2.1 The Concept of Elasticity of Demand ................................................................................. 10
2.2 Price Elasticity of Demand for New Automobiles ............................................................. 11
2.3 Research on the Effects of Vehicle and Fuel Incentives..................................................... 12
3.
The Incremental Vehicle Cost (IVC) Model ........................................................................ 13
3.1 Structure and Components of the IVC ................................................................................ 13
3.2 IVC Inputs and Assumptions .............................................................................................. 14
3.3 IVC Updates........................................................................................................................ 19
4.
The Market Share and Elasticity Spreadsheet Model ........................................................... 20
4.1 Vehicle Sales by Nameplate and Technology .................................................................... 20
4.2 Vehicle Rebates and Sales Taxes by Nameplate ................................................................ 22
4.3 Lost Sales and Incentives without State Rebates ................................................................ 23
4.4 Forecasts for 2015 to 2019.................................................................................................. 25
5.
The IMPLAN Model and Model Runs ................................................................................. 26
5.1 Overview and Benefits of the IMPLAN Model .................................................................. 26
5.2 General IMPLAN Modeling Considerations ...................................................................... 30
5.3 Stimulation of the IMPLAN Model .................................................................................... 31
5.3 IMPLAN Model Results ..................................................................................................... 36
6.
Results and Conclusions ....................................................................................................... 38
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Executive Summary
California’s Clean Vehicle Rebate Project (CVRP) encourages technologies and innovations that
provide immediate reductions of air pollution emission and that stimulate development and
deployment of sustainable transportation. They also stimulate economic activity that leads to
more plug-in electric vehicle (PEV) sales and more federal incentives flowing into the California
economy.
Over the next 5 years (2015-2019), ending the CVRP will save the state approximately $800
million, but will:
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Reduce EV sales by almost 95,000
Cost Californian’s $600 million in federal tax credits
Cost California governments $87 million in tax revenue
Eliminate over 8,000 job years
Reduce output by $2 billion and value added by $720 million
In the absence of the CVRP rebate, the Federal tax credit continues to support PHEV and BEV
sales that produce California jobs and GSP. However, losing the CVRP rebate has a substantial
impact on the positive impacts of larger EV sales on the economy as well as resulting in
additional gasoline consumption and GHG emissions.
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1. Introduction
California’s Clean Vehicle Rebate Project (CVRP) encourages technologies and innovations that
provide immediate reductions of air pollution emissions and that stimulate development and
deployment of sustainable transportation. The CVRP also stimulates economic activity that leads
to more plug-in electric vehicle (PEV) sales and more federal incentives flowing into the
California economy.
In addition to the state incentive for PEV purchase, the Federal Government provides for eligible
Plug-in hybrid-electric vehicles (PHEVs) purchased in or after 2010 a federal income tax credit
of up to $7,500. The credit amount will vary based on the capacity of the battery used to fuel the
vehicle. This is money flowing into the California economy producing increased economic
activity and employment in the state as well as increases in the state’s general fund.
The benefits of electric vehicle purchases in California to the California economy are substantial.
This study seeks to contribute to the ever-growing measurement and documentation of these
benefits of PEV sales that California would not realize without the federal and state new vehicle
purchase and lease incentives. For example, a University of California study estimated that for
15 and 45 percent PEV sales penetration scenarios, the resulting stimulus would add about
50,000 and 100,000 (net) new jobs by 2030, respectively. 1
1.1 Background on EV Incentives for California Purchasers
With the passage of AB32 and SB375, California embarked on the most ambitious program in
the country to reduce GHG emissions. The state has set GHG emissions goals that will require
major changes in how the state powers its industry, heats and cools it homes, and travels. In the
transportation sector, policy analysts often talk about the three avenues for GHG emissions
reductions: increased vehicle efficiency, reduced carbon intensity of fuels, and reduced
petroleum powered vehicle use. California is employing all available policies to meet the
aggressive GHG emissions reduction goals. Encouraging the purchase or lease of a PEV is part
of the solution. Federal and state policies, such as purchase incentives for PEVs, are an
important GHG policy, but they are also smart economic policies as the switch away from
petroleum transportation will save households and businesses money and allow them to purchase
other goods and services that will create more jobs and provide economic growth for the
California economy. This increased employment and economic growth will in turn increase tax
receipts to the state funds.
1
Plug-in Electric Vehicle Deployment in California: An Economic Assessment, David Roland-Holst, University of
California – Berkeley, September 2012.
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California Electric Vehicle Sales Incentives
HOV Lane Exemption: Qualified alternative fuel vehicles—including hydrogen, hybrid, and electric vehicles—may use
designated HOV lanes regardless of the number of occupants in the vehicle. Qualified vehicles are also exempt from toll
fees in High Occupancy Toll (HOT) lanes.
Alternative Fuel Vehicle Rebate Program: The Clean Vehicle Rebate Project (CVRP) offers rebates for the purchase or
lease of qualified vehicles. The rebates offer up to $2,500 for light-duty zero emission and plug-in hybrid vehicles that the
California Air Resources Board (ARB) has approved or certified.
Sales Tax Exclusion for Manufacturers: California’s Alternative Energy and Advanced Transportation Financing
Authority (CAEATFA) provides a sales tax exclusion for advanced manufacturers and manufacturers of alternative source
and advanced transportation products, components or systems.
Alternative Fuel Vehicle Rebate Program: The San Joaquin Valley Air Pollution Control District administers theDrive
Clean! Rebate Program, which provides rebates of up to $3,000 for the purchase or lease of eligible new vehicles,
including qualified natural gas and plug-in electric vehicles.
Alternative Fuel & Vehicle Incentives: Through the Alternative and Renewable Fuel Vehicle Technology Program, the
California Energy Commission provide financial incentives for businesses, vehicle and technology manufacturers,
workforce training partners, fleet owners, consumers and academic institutions with the goal of developing and deploying
alternative and renewable fuels and advanced transportation technologies.
Insurance Discount: Farmer’s Insurance offers a discount of up to 10 percent on certain insurance coverage for HEV and
AFV owners.
PEV Charging Rate Reductions: The Sacramento Municipal Utility District (SMUD), Southern California Edison (SCE),
Pacific Gas & Electric (PG&E), Los Angeles Department of Water and Power (LADWP), and San Diego Gas & Electric
(SDG&E) provide cost-of-service rate plans to commercial residential customers for electricity used to charge qualified
electric vehicles.
Electric Vehicle Supply Equipment Rebate: The Los Angeles Department of Water and Power (LADWP) Charge Up
L.A.! program provides rebates to residential and commercial customers who install Level 2 (240 Volt) chargers. Rebates
are offered to the first 2,000 customers who apply. Glendale Water and Power (GWP) also offers a $200 rebate to
residential customers owning an electric vehicle and installing a Level 2 charging station. Certain restrictions apply.
Free Parking: Sacramento offers free parking to individuals or small businesses certified by the city's Office of Small
Business Development that own or lease EVs with an EV parking pass in designated downtown parking garages and
surface lots. Vehicles must be 100 percent electric to qualify.
Free Parking: Free metered parking in San Jose, Hermosa Beach, and Santa Monica for electric vehicles displaying a
Clean Air decal.
Alternative Fuel Vehicle Parking: The California Department of General Services (DGS) and California Department of
Transportation (DOT) must provide 50 or more parking spaces and park-and-ride lots owned and operated by DOT
to incentivize the use of alternative fuel vehicles.
Adapted from National Conference of State Legislatures, State Efforts Promote Hybrid And Electric Vehicles
http://www.ncsl.org/research/energy/state-electric-vehicle-incentives-state-chart.aspx
Federal Incentives
Electric vehicles (EVs) that buyers purchased, in or after 2010, may be eligible for a federal
income tax credit of up to $7,500. The credit amount will vary based on the capacity of the
battery used to fuel the vehicle. This credit replaces an earlier credit for EVs purchased in 2009.
Some of the requirements for eligibility include: 2
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2
The vehicle must be made by a manufacturer (i.e., it doesn't include conventional
vehicles converted to electric drive)
Adapted from: www.fueleconomy.gov, the official U.S. government source for fuel economy information
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The vehicle must be treated as a motor vehicle for purposes of title II of the Clean Air
Act
The vehicle must have a gross vehicle weight rating (GVWR) of not more than 14,000
lbs
The vehicle must be propelled to a significant extent by an electric motor which draws
electricity from a battery which has a capacity of not less than four kilowatt hours and is
capable of being recharged from an external source of electricity
The vehicle commences with the taxpayer (it must be a new vehicle) and the vehicle is
acquired for use or lease by the taxpayer, and not for resale
The credit is only available to the original purchaser of a new PEV
The incentive declines as individual manufactures reach annual PEV sales sufficient to produce
scale economies of production that should allow them to offer PEV at competitive market prices.
The credit begins to phase out for vehicles at the beginning of the second calendar quarter after
the manufacturer produces 200,000 eligible plug-in electric vehicles (i.e., plug-in hybrids and
EVs) as counted from January 1, 2010. IRS will announce when a manufacturer exceeds this
production figure and will announce the subsequent phase out schedule.
California State Incentives
The Clean Vehicle Rebate Project (CVRP) encourages technologies and innovations that provide
for immediate reductions in air pollution emissions and stimulate development and deployment
of sustainable transportation. The CVRP, funded by the California Environmental Protection
Agency's Air Resources Board (ARB) and administered statewide by Center for Sustainable
Energy (CCSE), provides rebates of up to $2,500 for the purchase or lease of zero-emission and
plug-in hybrid light-duty vehicles. Rebates are available for individuals, nonprofits, government
entities and business owners.
In march 2014, following another record-breaking month of rebates for the cleanest cars in
California, the Air Resources Board (ARB) voted on April 25, 2014 to immediately expand the
funding and current waiting list for incentive funds that help consumers buy zero-emission and
plug-in hybrid vehicles. The ARB’s action expanded the current $5 million waiting list,
established in March, by an additional $25 million to accommodate expected market growth into
the summer. The move is in response to the growing demand for zero-emission and plug-in
hybrid electric vehicles over the past two years.
California is currently processing about 3,500 rebates each month totaling between $6 million
and $7 million. Exhibit 1-1 provide the month by month history of rebates processed thru
October 2014. March marked another record-setting month with 4,800 rebates totaling $9.8
million, according to CCSE transportation staff who administer the statewide Clean Vehicle
Rebate Project (CVRP). However they have been close to but have not reached this level since
that peak. The CVRP has exceeded all expectations. ARB has issued about $100 million for
about 50,000 rebates since the program began in March 2010 and plans to continue supporting
the project in the coming years.
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Exhibit 1-1: Electric Vehicle Rebates Processes by Month 3
1.2 Objectives of the Analysis
The objective of this study was to quantify some of the economic and financial benefits of the
CVRP in terms of capturing Federal tax incentives and increasing California’s general fund
revenues. The following subsections describe these analytical goals in more detail.
Federal Tax Incentives Captured by California
The federal incentives for PEV purchase are new funds flowing into the California economy.
For every PEV purchased, the state economy is enriched by the federal incentive. The first study
objective was to calculate, using traditional economic tools, the dollar value of these federal
3
Center for Sustainable Energy (2014). California Air Resources Board Clean Vehicle Rebate Project, Rebate
Statistics. Data last updated October 2014. Retrieved 10/30/2014 from http://energycenter.org/clean-vehiclerebate-project/rebate-statistics
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incentives and their impact on the California economy from the direct investment to the indirect
and induced (multiplier effects) economic impacts.
California government is a leader in the encouragement of cleaner and greener vehicles including
investment, technology, production and consumption. One of the benefits of this leadership is
the ability to attract investment, including Federal incentive dollars. These incentive dollars,
which taxpayers from all states pay for, flow disproportionally into California. For example,
California drivers accounted for just under one-third of all electric vehicles purchased in the
United States, 4 representing roughly two and a half times California’s share of population. One
reason for this leadership role is the state’s aggressive rebate program.
Recent data show that during 2013, Californians registered 42,198 plug-in and electric vehicles. 5
The purchasers of these vehicles were eligible for over $270 million in Federal tax credits. This
is an estimate for 2013, but these numbers are currently on the rise.
This research presents the latest consensus data on plug-in and electric vehicle sales estimates
and penetration scenarios for California. This includes the PEV15 and PEV45 scenarios the UC
Berkeley study used as well as other available scenarios and assumptions. The research team
estimated sales by type of vehicle (plug-in, all electric) and year to estimate the total value of
federal incentives brought to California.
These direct flows of vehicle purchase rebates and incentives to California consumers, however,
are not the end of the story. These monetary flows will circulate through the California economy
creating further indirect and induced impacts. The research team modeled these impacts using
IMPLAN, the most widely employed economic impact tool by economists across the country.
These estimates are arrayed by type of vehicle (plug-in, all electric), year, and by type of impact
(direct, indirect, induced).
Additional General Fund Revenue
Government budgets have been under unusual strain over the last five years and legislators
across the country are examining any and every means to raise revenue, reduce spending and cut
deficits. However, spending cuts should not be indiscriminate, as some spending cuts could
actually reduce revenue in the longer run and have other negative consequences as well.
The Clean Vehicle Rebate Program (CVRP) and the California Hybrid and Zero Emission Truck
and Bus Voucher Incentive Project (HVIP) are potentially programs of this type. While these
programs cost the State of California money in the form of direct rebates, they also encourage
PEV purchases, which provide a variety of benefits not only to the state economy but also to the
state general fund. The CVRP is a positive investment increasing federal funds to the state and
stimulating other economic activity.
4
“Electric Cars: California Leads Nation In Green Vehicle Consumption,” Aaron Sankin, Huffingto Post, November
11, 2012.
5
California Auto Outlook, CNCDA, Vol. 10, No 1, February 2014.
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The research team conducted a thorough literature review of vehicle incentive programs. This
review included prior studies on the costs, cost‐effectiveness and benefit-cost of California
vehicle incentives programs. Literature for all types of alternate fueled vehicles was included,
not just citations for plug-in or all electric vehicles. The research team considered the findings
from the literature review in developing the study methodology and in estimating the benefits of
these investments to the state’s economy and general fund.
As is often the case, simply assessing the dollar value of an incentive is insufficient to fully grasp
the general fund impacts, as the economic stimulus will also increase revenue to the state.
As such, research team used the Social Accounting Matrix (SAM) of the IMPLAN model to
estimate the general fund revenue impacts of the incentives and its’ direct, indirect and induced
effects. The estimates are arrayed by type of vehicle (plug-in, all electric), year, and by type of
impact (direct, indirect, induced).
The presence of the CVRP may provide the marginal incentive that convinces buyers to purchase
a PEV or EV as opposed to a conventionally fueled vehicle. Therefore, the research team used
results of the literature review to estimate EV price elasticity and to calculate the effect of the
presence or removal of the CVRP/HVIP incentives on EV sales, state economic activity and state
general revenues. The research team designed the analysis to support the hypothesis that the
incentive programs are vital contributors to the growth and acceleration of the PEV market and
will ultimately lead to increases in general fund revenues while providing jobs, air quality,
competitiveness, environmental justice and GHG reduction.
1.3 Previous Research by U.C. Berkeley
The major previous assessment of the macroeconomic effects of electric vehicles on the
California economy was the U.C. Berkeley report by David Roland-Holst. This report was part
of a series of research studies into alternative energy and resource pathways for the global
economy. In addition to disseminating original research findings, the U.C. Berkeley authors
intended this series of studies to contribute to policy dialog and public awareness about
environment-economy linkages and sustainable growth. To appraise the economic impacts of
PEV deployment on the California economy, the authors used a state-of-the-art forecasting tool,
the Berkeley Energy and Resources (BEAR) model. After calibrating it to detailed data on the
state economy, vehicle fleet, and related information, the authors evaluated a set of three policy
scenarios:
1. Baseline: Assume California implements current commitments to state and post-1990
federal fuel economy standards, but continues growth at levels forecast by the
Department of Finance
2. PEV15: Including the Baseline scenarios, but assume 15.4 percent PEV deployment in
the new light-duty vehicle fleet by 2030. This level of penetration would be consistent
with fulfillment of ZEV regulations by PEVs. Tax credits for PEV vehicles are phased
out by 2020, and LCFS credits are awarded for pollution reduction
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3. PEV45: Same as PEV15, except PEV deployment is accelerated to 45 percent of the new
light-duty vehicle fleet by 2030
Exhibits 1-2 and 1-3 present the long-term aggregate economic effects of the three vehicle
deployment alternatives. According to the authors, these projections “indicate that new vehicle
technologies, particularly those that reduce reliance on fossil fuels, stimulate economic growth
and job creation.” The most robust finding of this study is that statewide economic growth and
employment rise with the degree and scope of PEV adoption. According to the authors, “What
matters is that PEV technologies have positive net value to those who adopt them. When vehicle
owners realize these savings, be they households or enterprises, they will reappear as demand for
goods and services outside the petroleum fuel supply chain, and the result will be higher state
economic growth and employment.”
Exhibit 1-2: PEV Employment Impacts
Exhibit 1-3: Statewide Macroeconomic Impacts
PEV15
PEV45
Real GSP
4.954
8.177
Net Job Growth
48,816
97,761
Source: Authors’ estimates
Notes: Real Gross State Product (GSP, dollar billions) and Employment (FTE)
are expressed as changes from Baseline values in 2030.
1.4 Organization of the Report
The organization of the remainder of this report includes four chapters. Chapter 2, Elasticity
Estimates, provides an overview of the elasticity literature and the sources and estimates this
study uses to calculate the effect of removing rebates on EV sales.
Chapter 3, the IVC Model, informs the reader as to the basic functioning and structure of the
Incremental Vehicle Cost (IVC) model, as well as the major assumptions that its developers
employed.
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Chapter 4, the Market Share and Elasticity Spreadsheet Model, documents the spreadsheet that
this study uses to develop estimates of 2013 California EV sales, rebates and incentives, with and
without rebates in both units and dollars.
Chapter 5, the IMPLAN Model and Model Runs, provides an overview of the IMPLAN model,
describes how it was stimulated for this study, and describes the model calibration and results.
Chapter 6 provides study results and conclusions.
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2. New Vehicle Elasticity Estimates
This chapter provides an overview of the elasticity literature and the sources and estimates this
study uses to calculate the effect of removing rebates on EV sales. The first section provides an
overview of the concept of elasticity of demand. The second section discusses applicable
research on the price elasticity of demand for automobiles. The third section reviews prior
studies on the costs, cost‐effectiveness and benefit-cost of vehicle and fuel incentive programs.
2.1 The Concept of Elasticity of Demand
The Federal and California electric vehicle incentive programs increase the number of electric
vehicles sold. The increase in electric vehicle sales may increase total vehicle sales or it may
shift sales from gasoline-powered vehicles to electric powered vehicles. The two incentive
programs lower the cost of electric vehicle purchase by providing cash rebates in the California
case and tax credits in the federal case. Neither are point-of-sale rebates as the California
program requires application and approval that may delay delivery of the rebate for several
months and the federal program provides a tax credit, which is available when the buyer files his
tax return for that year which could be a year or more after vehicle sale.
This analysis considers the impact on the California economy of a suspension of the California
electric vehicle rebate program. A suspension of the California rebate program will increase the
effective price of electric vehicles and thus reduce the expected sales. Economics refers to the
sensitivity of the sales of a product or service to a change in price as the price elasticity of
demand (PED). Specifically, the PED is the ratio of the percent change in demand to the percent
change in price. So, for example, if the demand for a product falls by five percent as a result of a
two percent increase in price, the price elasticity of demand for this product would be two and
one half (-5 percent/2 percent = -2.5). PED are usually negative, that is that as price rises, sales
fall, although presentations of PED often drop the sign. Economics describes the demand for a
good as inelastic when the PED is less than one (in absolute value). In this case changes in price
have a relatively small effect on the quantity of the good demanded. Economics describes
demand for a good as elastic when its PED is greater than one (in absolute value). In this case,
changes in price have a relatively large effect on the quantity of a good demanded.
Many factors influence consumer demand for new vehicles. These include the manufacturer
suggested retail price, manufacturer and dealer incentives, government incentives, taxes and fees,
technological advances, the state of the economy, and other factors. The demand for electric
vehicles may also be impacted by risk aversion to new technology (e.g. battery life/warranty) and
“doing your part” for the environment. Consumers can postpone purchases into the future by
holding on to their current vehicles longer, waiting to purchase a second household vehicle or
choosing an alternative mode of transportation. In economic parlance, this makes the demand
for new automobiles elastic. Higher prices can retard sales where price reductions can encourage
sales that consumers might otherwise postpone. Automobile manufacturers and dealers choose
vehicle prices and incentives to increase sales.
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Once a household has decided to make a new vehicle purchase and has identified the class of
vehicles that best meets its needs and financial resources, selection of a particular vehicle within
a class is very sensitive to price. Research by academics, institutions and firms has shown that
small changes in the price of like products, including automobiles, can result in substantial shifts
among in-class sales. Consumers of smaller vehicle classes such as compacts and subcompacts
exhibit pronounced sensitivity to vehicle prices. The more alike products become in their
attributes such as interior volume, efficiency, power, reliability, etc., the more sensitive sales are
to the price of like or even similar vehicles.
2.2 Price Elasticity of Demand for New Automobiles
The purchase of a new automobile is generally the second largest household purchase after a
home. The automobile industry is one of the largest manufacturing sectors in the US economy
and has a substantial influence on many other sectors of the economy. Thus understanding PED
is very important for both the public and private sector planning. Electric vehicles can provide a
very similar service to that provided by internal combustion engine powered vehicles, but there
are many significant differences, which complicates estimating the PED for electric vehicles. In
addition, since the technology is evolving rapidly due to public and private investments, the PED
will be changing over time as the technology and the public’s understanding of it evolves.
The goal of this study is to estimate the macroeconomic impacts (statewide employment,
household income, value added/state product) of a change in sales brought about by an
elimination of the state incentive program. Forecasting the total sales or sales by nameplate is
very challenging as there are many influences on future sales and these influences themselves are
hard to predict. In this study, the goal was a much simpler task, predicting the change in a subset
of sales given a change in a single variable, all others held constant. The analysis only needed to
create a straw man future sales level and test that level for a change, given a change in the
incentive program. The amount of uncertainty in the change is much smaller than the amount of
uncertainty in the total sales.
This analysis incorporates the assumption that the suspension of the California EV rebate
program will reduce expected EV sales. Estimating PED for vehicles requires an extensive
database of vehicle sales, attributes, prices and other factors such as licensed drivers, household
income, and economic growth. These data are not yet available for a significant volume of
electric vehicle sales. As a result, this research sought a typical estimate of price elasticity of
demand for vehicles. General Motors Research Staff conducted one of the most extensive
studies of vehicle elasticity in the early 1990s and has periodically updated this study, with the
most recent update as recently as 2006. In these studies, Dr. Robert Brodley had access to an
extensive database of over 40,000 new vehicle sales transactions. Dr. Brodley estimated a
number of statistical models to estimate elasticity for individual classes including their own and
cross price elasticity. For this analysis, the research team chose the estimated average own price
elasticity of demand from Dr. Brodley’s study of -3.6 percent. Dr. Brodley also estimated
elasticity by vehicle class (including economy, compact, small, midsize, large, luxury and sport),
with estimates ranging from -2.4 to -4.7. The detailed elasticity estimates by class are less
statistically reliable and were not used in this analysis.
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Additional researchers have developed estimates of elasticity, which are generally consistent
with Dr. Brodley’s findings. Examples include a recent study 6 by the Congressional Budget
Office to evaluate the impact of the federal incentive program on electric vehicle sales. While
the CBO did not publish a specific EV elasticity estimate, the implied elasticity in their price
impacts and sales forecasts are in the range of -1.6 to -2.0.
2.3 Research on the Effects of Vehicle and Fuel Incentives
Financial incentives to encourage the development and purchase of more fuel-efficient vehicles
have been a part of the public policy approach to reducing transportation fuel consumption and
GHG emissions for some time. Governments have directed some of the incentives at
manufactures and others at consumers. Incentives to manufactures encourage the development
of more fuel-efficient vehicle and engine design while consumer incentives target shifting
consumer preferences to more fuel efficient vehicles.
6
Congress of The United States, Congressional Budget Office (CBO), Effects of Federal Tax Credits for the
Purchase of Electric Vehicles, September 2012.
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3. The Incremental Vehicle Cost (IVC) Model
The previous U.C. Berkeley economic assessment of California electric vehicle deployment
relied on the BEAR model. BEAR models the overall economy, but it does not detail vehicle
costs and benefits at the individual level, especially with respect to emerging technologies and
vehicle diversity. To calibrate this component of the estimation procedure, the researchers at
U.C. Berkeley created a detailed Incremental Vehicle Cost (IVC) spreadsheet model. This
entailed collection and synthesis of the most up-to-date information on available present and
future vehicle technologies, a wide range of analysis and assumptions regarding ownership and
use behavior, and assumptions about forward market conditions.
The analysis of the macroeconomic impacts this study presents uses the outputs of the IVC
model, modified to reflect the latest data. This chapter presents a variety of salient information to
inform the reader as to the basic functioning and structure of the IVC, as well as the major
assumptions that its developers employed. Section 3.1 details the major components
(worksheets) in the model. Section 3.2 describes the IVC’s major inputs and assumptions.
Section 3.2 describes changes that the authors of this report made to the IVC.
3.1 Structure and Components of the IVC
The IVC Spreadsheet Model constructs inventories of electric and conventional vehicles for
three scenarios, by year, vehicle type and vehicle age. These scenarios are combined with data on
vehicle miles of travel (VMT) by age of vehicle, fuel efficiency by vehicle type and vintage,
price forecasts by fuel type, vehicle purchase prices, rebate and tax credit assumptions, and other
variables to produce a data set that the U.C. Berkeley researchers used to stimulate the BEAR
model.
The IVC Spreadsheet Model is comprised of 19 worksheets. The IVC labels three of these
worksheets as “old” and one additional worksheet has a redundant purpose and no longer appears
linked to the other worksheets. The following list summaries the other 15 worksheets including
their names and basic functions.
1. Inputs & Results (financed): This worksheet compiles and summarizes data from the
other worksheets for use in summary graphics.
2. BEV-PHEV_Adoption: This worksheet interpolates sales for electric vehicles between
2015 and 2030 using a logistic function. Sales for 2015 are from CARB estimates and
sales for 2030 are target sales by scenario.
3. FC_15%: This worksheet calculates fuel use by fuel type using estimates of VMT, MPG,
Survival rates and percentages of vehicles by type for the 15 percent PEV deployment
scenario.
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4. FC_45%: This worksheet calculates fuel use by fuel type using estimates of VMT, MPG,
Survival rates and percentages of vehicles by type for the 45 percent PEV deployment
scenario.
5. IVCDat: This worksheet collects each of the data series that the BEAR model used as
inputs.
6. Costs: This worksheet calculates incremental costs by year and scenario using data on
EV stocks, flows, and unit costs.
7. Incentives: This worksheet calculates average California and Federal incentives per year
and applies them to sales from worksheet 2 to calculate total incentives for each year and
scenario.
8. CO2: This worksheet calculates CO2 emissions factors for California gasoline and
electricity.
9. CA_GasForecast: This worksheet estimates California specific gasoline and diesel price
forecasts.
10. CA_ElecForecast: This worksheet estimates California specific retail price forecasts for
electricity.
11. baseline_EPA: This worksheet tabulates EPA data on national fleet averages, EV energy
use characteristics, EV incremental prices, and direct manufacturing costs for batteries.
12. FE_MIT: This worksheet tabulates MIT data to forecast fuel consumption and mpg
relative to current vehicles for eight technologies for passenger cars and light trucks.
13. GasolineElecPrice: This worksheet tabulates US Department of Energy data on
electricity and gasoline prices from the Annual Energy Outlook.
14. CA-GasPremium: This worksheet tabulates data on California versus US gasoline prices
to examine the percentage California markup.
15. VMT: This worksheet uses estimate of VMT per vehicle by age and number of vehicles
by age in each year to estimate total VMT by year and age of vehicle.
3.2 IVC Inputs and Assumptions
This section provides an overview of some of the key inputs and assumptions that the IVC uses
in calculating the various inputs that the U.C. Berkeley researchers used in assessing the
economic impacts of California electric vehicle deployment.
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Vehicle Technologies
Purchasers of automobiles and light trucks have an expanded array of model choices, both in
Internal Combustion Engine (ICE) and PEV technologies. It is impractical for the IVC to track
all these alternatives. However, vehicle choice is a primary determinant of the outcomes
considered, and therefore the IVC specifies enough vehicle diversity to capture this.
To that end, IVC considers representative ICE categories for cars and light trucks, reflecting
average performance characteristics over the period considered (2012-2030). The IVC models a
significant improvement in fuel economy over time. The IVC assumes that new ICE passenger
cars will achieve close to 45 miles per gallon (real world) by model year 2025. The IVC assumes
that new ICE light trucks will achieve 32 miles per gallon by model year 2025. This is
consistent with the joint rulemaking between the U.S. Environmental Protection Agency and the
U.S. National Highway Traffic Safety Administration. The IVC assumes this representative
vehicle type consumes a blend of gasoline and diesel reflecting the shares of both fuel types in
the light vehicle market.
In addition to this, for both cars and light trucks, the IVC considers three PEV categories,
PHEV20, PHEV40, and BEV100. While there are increasing sub varieties of electric vehicles
appearing on the market, these three varieties capture current offerings and capture the essential
heterogeneity of the PEV fleet.
Adoption and Use Behavior - Demand Growth
The IVC does not model vehicle demand or adoption at the individual owner level. Instead, the
model estimates the impacts of policy scenarios that assume a given pattern of adoption,
specified in terms of target sales or market shares in the terminal year 2030. In addition to this,
the IVC calibrates baseline PEV demand shares to match CARB estimates for 2015.
Between 2015 and the 2030 target, the IVC interpolates demand using a logistic function, the
standard profile for technology adoption studies. The IVC estimates three adoption patterns
including accelerated (Early), as with fashionable consumer goods, or more gradual (Late),
where significant learning might be required for adoption, and a median case (Normal) that is
typical of most successful automotive technology. The U.C. Berkeley researchers used the
median case (Normal) for the version of the IVC used in assessing the economic impacts of
California electric vehicle deployment.
Adoption and Use Behavior – PEV Fleet Composition
The IVC considers six generic PEV vehicle types, cars and light trucks in three electric power
categories. Having little reliable empirical evidence on the forward composition of this market,
the U.C. Berkeley researchers chose generic shares to capture diversity of the fleet. Exhibit 3-1
lists the vehicle and technology categories and the market shares attributed to each.
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Exhibit 3.1:
Assumed Shares of New PEV Sales
Vehicle Type
Passenger
Cars
Light Trucks
Technology
Type
PHEV20
PHEV40
BEV100
PHEV20
PHEV40
BEV100
Share of Vehicle
Type
33%
33%
33%
50%
30%
20%
The U.C. Berkeley researchers noted that depending on the price of fuel as well as innovation
trends, more efficient vehicles could be expected to gain larger market share. In the one to two
years, since the U.C. Berkeley researchers conducted the ETC study, the electric vehicle market
in California has expanded significantly and BEV’s have captured a larger share of sales.
Chapter 4 of this report presents this more recent data and changes to the IVC assumptions that
the present study has adopted.
Adoption and Use Behavior - Vehicle Miles Traveled
Costs of vehicle operation, particularly fuel costs and savings from PEVs, depend significantly
on vehicle use levels, of which vehicle miles traveled (VMT) is the primary indicator. Actual
VMT vary with a myriad of vehicle and user characteristics. For practical purposes, the IVC
relies on averages from CARB, implemented for each model year and over the life of all vehicles
sold in each model year.
VMT will also vary with model of car or light truck, but again the IVC relies on averages for the
current estimates. Some evidence suggested that the early PEV owners were driving fewer VMT
than owners of ICE models. Accordingly, the IVC assumes that owners of battery only electric
vehicles (BEVs) travel fewer VMT, but that this will change as battery technology improves.
The IVC assumes that BEV owners drive 58 percent as many miles as ICE owners in 2015,
climbing to 90 percent in 2020 and then remaining at that level.
Adoption and Use Behavior - Survival
Vehicle life expectancy varies with technology and driving conditions. The IVC uses a single
survival rate function for all vehicles. For this component of fleet turnover, the IVC uses survival
rates for each model year following trends published by the National Highway Traffic Safety
Administration. Exhibit 3-2 graphically portrays the NHTSA vehicle survival rates.
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Exhibit 3-2 NHTSA Vehicle Survival Rates
1.20
1.00
0.80
0.60
0.40
0.20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Financing
The overwhelming majority of new light vehicle sales include financing (90 percent according to
the National Automobile Dealers Association). In the context of PEV adoption, these options are
quite important because it gives buyers the option to pay for their vehicle in significant part with
fuel savings enjoyed after purchase. To capture this, the U.C. Berkeley researchers assumed a
relatively simple finance model, allowing buyers of PEVs to pay the full cost of their vehicles in
equal installments over five years, inclusive of a 5 percent APR premium. This interest rate is
relatively conservative by the standards of recent years.
Energy Costs
The two primary energy sources relevant to the IVC analysis are liquid transportation fuels and
electric power. In the transport category, the primary fuels are gasoline and diesel. The U.C.
Berkeley researchers assumed global energy markets are independent of the policy scenarios,
and that they drive national fuel prices according to trends set forth in the EIA’s Annual Energy
Outlook (early release, 2012). The U.C. Berkeley researchers also assumed that California fuel
prices would follow their historical pattern of being higher than national average prices.
For electric power price trends over the period considered, the U.C. Berkeley researchers relied
on a combination of EIA data forecasting Pacific region and California electricity prices.
Vehicle Costs
The IVC assumptions regarding vehicles explicitly recognize innovation processes and changing
vehicle standards over the time-period considered. To this end, the U.C. Berkeley researchers
assumed that ICE vehicles attain higher average mpg in accordance with EPA and NHTSA’s
National Program harmonizing greenhouse gas and fuel economy standards, and that conformity
with these confers incremental costs for ICE vehicles. The IVC utilizes EPA and NHTSA’s
incremental cost estimates of $2,811 for MY2025 passenger cars and $3,052 for MY2025
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passenger trucks versus today’s new vehicles. For PEV vehicles, the U.C. Berkeley researchers
built the IVC estimates from the bottom up, using the most up-to-date electric vehicle technology
data available, as the following paragraphs describe.
Batteries are a primary cost component in all PEVs, and the IVC assumes steady but moderate
progress or “learning” in this technology. The IVC uses projections by McKinsey and Company
to estimate battery costs. The analysis adjusted the usable state of charge window from a fixed
value of 70% to values of 60% for the PHEV10, 70% for the PHEV40, and 90% for the BEV100
to reflect the different usage characteristics of each vehicle type. The analysis scaled battery
costs by 1.5 for the PHEV10 and 1.3 for the PHEV40 to reflect the increased costs to meet power
requirements with smaller batteries using EPRI estimates.
The IVC used an indirect cost multiplier (ICM) of 1.5 to mark-up the direct costs to retail. ICMs
generally account for indirect costs such as research and development, overhead, dealer markup,
warranty, and dealer profit as documented by EPA, NAS, and CARB. The IVC based component
costs for PEVs on the U.S. Environmental Protection Agency and National Transportation
Highway Safety Association regulatory impact analysis. The U.C. Berkeley researchers believe
the ICMs and component mark ups utilized in the IVC are conservative. The result is a
cost/efficiency improvement of about 80 percent over the next two decades.
After a review of the vehicle engineering literature and consultation with experts in this field, the
U.C. Berkeley researchers estimated incremental vehicle cost for PEVs using these battery cost
profiles and a 30 percent mark-up on other power and drive train components.
Incentives
The U.C. Berkeley researchers incorporated current incentive schemes on a uniform average
basis into the IVC model. The model assumes that the state and federal government does not
renew these incentives beyond 2020. In the BEAR model, the cost of fiscal incentives, in terms
of foregone income/expenditure elsewhere in the economy, is fully accounted. The
macroeconomic impact results of the U.C. Berkeley study are thus net of these transfer effects.
LCFS Credits
PEV owners are contributing to global greenhouse mitigation by reducing carbon fuel
consumption. Under California’s Low Carbon Fuel Standard (LCFS) regulations, providers of
electricity as a fuel are required to return value derived from the sale of LCFS credits to PEV
drivers. The IVC incorporates these credits into the net IVC calculations using CARB formulas
for vehicle efficiency and emissions factors. CARB estimates that LCFS credit value to be
between $15 and $50 per metric tonne of CO2 displaced. Therefore, the IVC uses the average
($32.50) to calculate the value of the credit.
Aggregate Trends
Including all the market, technology, and behavioral information above, the IVC model
calculates net economic returns to PEV deployment in both the PEV15 and PEV45 scenarios.
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The overall aggregate trends for IVC components show a strong positive dividend for those who
adopt these vehicles, as well as support for the industries developing, selling, and maintaining
them. By 2030, PEV vehicles are saving California households over $1.5 billion per year, with
cumulative savings since 2012 of over $6 billion. Under the 45 percent deployment scenario
(PEV45), annual savings by 2030 are over $3.5 billion and cumulative household savings exceed
$13 billion. With positive net savings (green trend) in nearly every year considered, electric
vehicles represent a long-term stimulus package for the state economy.
These figures represent aggregate private costs and benefits of PEV vehicles. For individual
buyers, financing defers costs over five years, while their incentive payments accrue upon
purchase. LCFS credits accrue over the useful life of their vehicles. From an aggregate
perspective, positive private net benefits are strongly supported in later years by legacy adoption
(fuel savings), but individual owners will face net costs upon purchase and have to overcome
these in later years of ownership.
3.3 IVC Updates
For this study, the research team accepted the majority of the data and assumptions that the U.C.
Berkeley researchers used in constructing the IVC. Those researchers recently constructed the
IVC and only slight or insignificant changes were likely in the majority of the assumptions and
data. The major area of change was the new information on California EV sales and EV
technology trends. In particular, sales of PEVs in California have accelerated more rapidly than
many would have predicted, climbing from only 397 and 6,984 in 2010 and 2011, respectively to
20,898 in 2012 and to an astounding 42,198 in 2013. By contrast, the IVC and the U.C. Berkeley
reports, completed in 2012, assumed 2013 PEV sales of 22,339, only 52.9 percent of actual sales.
Actual sales are in fact in line with the IVC’s more aggressive 45 percent penetration scenario.
Another phenomenon that the IVC did not foresee was the penetration of battery only (BEV
vehicles). While, the IVC assumes sales that are evenly distributed among PHEV20, PHEV40,
and BEV100 vehicles, the BEV100 vehicles have actually accounted for 52.0 percent of sales.
The following chapter analyzes data on sales and prices of California EVs. Where appropriate,
the research team adjusted data and assumptions in the IVC to reflect newly available
information.
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4. The Market Share and Elasticity Spreadsheet Model
In order to update the IVC and calculate several other data items for inclusion in the analysis, the
research team developed a spreadsheet model to supplement the IVC. This spreadsheet develops
estimates of 2013 California electric vehicle sales by model in both units and dollars, calculates
California and Federal rebates and incentives, estimates impacts of eliminating the state’s
electric vehicle incentives, and then forecasts key variables for the years 2015 to 2019. The
model includes four main components. They include portions that estimate:
•
•
•
•
2013 California electric vehicle sales by model and technology
2013 California electric vehicle rebates and sales taxes by model
2013 California electric vehicle lost sales and rebates without state electric vehicle
incentives
2015-2019 forecasts
The following four sections discuss each of these components.
4.1 Vehicle Sales by Nameplate and Technology
Complete data on the sales of EVs in California by technology type, in both units and dollars, are
not available from a single source. These data, however, are required as incentives vary by type
of EV, sales prices vary by model, and the location of EV production/assembly varies by
manufacturer.
The most complete and reliable estimates of EV sales in California is the data published by the
California New Car Dealers Association (CNCDA). Exhibit 4-1 provides the data the CNCDA
publishes in the quarterly California Auto Outlook. R.L. Polk provides the majority of this data
although CNCDA estimate registrations for the Ford CX-Max Energi and the Ford Fusion
Energi. The California Auto Outlook also provides data on Tesla Model S registrations in their
top selling models table, as it is one of the top five models in its’ segment. In 2013, Tesla Mosel
S registrations were 8,347. Research team staff contacted the CNCDA and inquired whether
additional unpublished data were available, but CNCDA staff indicated that they had no
additional details.
Exhibit 4-1: 2013 California Electric Vehicle Registrations
Category
2009
2010
2011
2012
2013
Plug in Hybrid
0
97
1,682 14,701 20,235
Registrations
Electric Registrations
772
300
5,302
6,197 21,963
Total
772
397
6,984 20,898 42,198
California Auto Outlook, CNCDA, Vol. 10, No 1, February 2014.
To supplement the CNCDA data, the research team tabulated data available from “Inside EVs,
the largest Internet property exclusively covering plug-in vehicles. This data source includes a
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list of all of the EVs sold, their range, national sales, and Manufacturer’s Suggested Retail Price
(MSRP). The research team used these data to disaggregate and extend the CNCDA data.
Exhibit 4-2 provides the portion of the study spreadsheet that estimates California electric
vehicle unit and dollar sales by model and technology. The first three columns of the exhibit list
each vehicle model and provide its electric battery range and technology type. The forth column
lists national PHEV unit sales by model from Inside EVs. The fifth column develops an estimate
of California PHEV sales for each model, by distributing the CNCDA California PHEV
registration estimate of 20,235 among models based on national sales by model. This final
calculation includes the observation from the Annual Incentive Program report that nine percent
of sales are for leases of less than 36 months which do not qualify for the incentive. 7 The sixth
and seventh columns separately sum up the estimates for California sales of PHEV-20s and
PHEV-40s. Column 8 lists national BEV sales by model from Inside EVs. The ninth column
develops an estimate of California BEV sales for each model, by distributing the CNCDA
California BEV registration estimate of 21,963 among models based on national sales by model
with the exception of Tesla sales, which were not directly available from CNCDA, as described
above. Column 10 provides the final estimates of California EV sales by model. Column 11 lists
the MSRP for each model from Inside EVs. The MSRP for the Tesla is a weighted average of the
MSRP for the two available battery sizes with the larger battery receiving a two-thirds weight. 8
Column 12 provides dollar sales, which are the product of the previous two columns.
Exhibit 4-2: 2013 California Electric Vehicle Sales by Model and Technology
7
Clean Vehicle Rebate Project, Fiscal Year 2012-2013 Final Report, December 2, 2013, Prepared by the Center for
Sustainable Energy under Grant G12-AQIP-01 for the California Air Resources Board, p17.
8
The research team based this weighting on rebate data by Tesla model from Exhibit 1 of the Clean Vehicle Rebate
Project, FY 2012-2013 Final Report, Prepared by the Center for Sustainable Energy California for the California Air
Resources Board, December 2, 2013.
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The bottom row of Exhibit 4-2, labeled “Share,” provides some crucial data for the overall
analysis. As discussed in Chapter 3, the IVC assumed that PHEV-20s, PHEV-40s and BEVs
each had a third of the EV market shares. However, Exhibit 4-2 shows that the market shares in
2013 actual tilt toward EVs with higher electric shares. PHEV-20s captured only 22.6 percent of
the EV market, PHEV-40s captured only 25.4 percent of the EV market, and BEVs captured
52.0 percent of the EV market. The research team used these percentages to update the
assumptions in the IVC.
The share cell in the last column reveals that Tesla accounted for just over a third of the dollar
sales of EVs in California. This is important as there are economic impacts on the California
economy of vehicle production and assembly to California. In addition, California is well
positioned to be a key supplier of the EV production industry nationwide.
4.2 Vehicle Rebates and Sales Taxes by Nameplate
Exhibit 4-3 provides the portion of the study spreadsheet that estimates 2013 California electric
vehicle rebates and sales taxes by model. The first three columns of the exhibit list each vehicle
and provide its electric battery range and technology type. Column 4 provides the final estimates
of California EV sales by nameplate, taken from Column 10 of Exhibit 4-2. Column 5 provides
the 2013 price after Federal tax credit, from Inside EVs, The price after Federal tax credit for the
Tesla is the weighted average of the MSRP for the two available battery sizes and the research
team estimated the prices for the Porsche and the Cadillac. Column 6 provides the amount of the
Federal tax credit per vehicle, which the spreadsheet calculates by subtracting Column 5 from
Column 11 of Exhibit 4-2, which provided MSRPs. Column 7 provides the dollar value of
Federal tax credits, which the spreadsheet calculates by multiplying Column 5 by Column 6.
Column 8 provides the amount of the California rebate per vehicle, which was available directly
from the Sustainable Energy California website. Column 9 provides the dollar value of
California Rebates, which the spreadsheet calculates by taking the product of Column 7, Column
8 and a factor of 0.76. The factor represents the percentage of drivers of eligible vehicles that
obtained a rebate. 9 Column 10 provides state sales tax income from the sales of the vehicles,
which the spreadsheet calculates by multiplying Column 12 of Exhibit 4-2 by the state sales tax
rate of 7.5 percent. Column 11 provides local sales tax income from the sales of the vehicles,
which the spreadsheet calculates by multiplying Column 12 of Exhibit 4-2 by an estimate of the
average local sales tax rate of 1.25 percent. The research team estimated the average local sales
tax rate of 1.25 percent using data from the California State Board of Equalization.
9
The Clean Vehicle Rebate Project, FY 2012-2013 Final Report, Prepared by the Center for Sustainable Energy
California for the California Air Resources Board, December 2, 2013, notes that “By comparing rebate data with
DMV registration data, it was found that 76% of drivers of eligible vehicles (14,736 out of 18,438) in the study
period obtained a rebate.”
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Exhibit 4-3: 2013 California Electric Vehicle Rebates and Sales Taxes by Model
EV
Vehicle Model Range
Chevy Volt
38
Nissan Leaf
75
Tesla Model S
208-265
Toyota PiP
11
Ford CX-Max Energi
21
Ford Fusion Energi
21
Ford Focus Electric
76
Toyota RAV4 EV
103
Mitsubishi I-MiEV
62
Smart ED
87
Fiat 500e
87
Honda Fit EV
82
Chevrolet Spark EV
82
Honda Accord PHV
13
Porshe PanameraS-E
N.A
Cadillac ELR
N.A
Total
Vehicle
Type
PHEV-40
BEV-100
BEV-100
PHEV-20
PHEV-20
PHEV-20
PHEV-100
BEV-100
PHEV-100
BEV-100
BEV-100
BEV-100
BEV-100
PHEV-20
PHEV-20
PHEV-40
2013 2013 Price Federal
California
After Incentive
PHEV/BEV
Federal
Per
Unit Sales
Credit Vehicle
9,529
27,495
7,500
10,562
21,300
7,500
8,347
61,567
7,500
4,987
27,490
2,500
2,952
29,200
3,750
2,512
34,950
3,750
812
27,700
7,500
512
42,300
7,500
481
21,625
7,500
431
17,500
7,500
301
25,000
7,500
266
29,915
7,500
252
19,185
7,500
217
36,030
3,750
35
95,250
3,750
2
72,245
3,750
42,198
33,312 6,400.81
California
Rebate
Per
Federal
Vehicle
Incentive
71,463,882
1500
79,211,403
2500
62,602,500
2500
12,468,685
1500
11,068,949
1500
9,421,139
1500
6,088,873
2500
3,839,704
2500
3,604,977
2500
3,233,619
2500
2,259,680
2500
1,993,423
2500
1,888,321
2500
813,848
1500
133,063
1500
9,283
1500
270,101,349 1,535.56
California
State Sales
Rebate Taxes @7.5%
10,862,510
25,008,786
20,066,889
22,812,884
15,859,300
43,237,460
5,685,720
11,218,076
3,364,960
7,294,437
2,864,026
7,291,962
1,542,515
2,143,283
972,725
1,912,172
913,261
1,049,950
819,183
808,405
572,452
734,396
505,000
745,839
478,375
503,899
247,410
647,497
40,451
263,464
2,822
14,110
64,797,600 125,686,619
Local Sales
Taxes
@1.25%
4,168,131
3,802,147
7,206,243
1,869,679
1,215,740
1,215,327
357,214
318,695
174,992
134,734
122,399
124,307
83,983
107,916
43,911
2,352
20,947,770
4.3 Lost Sales and Incentives without State Rebates
Exhibit 4-4 provides the portion of the study spreadsheet that estimates 2013 California electric
vehicle lost sales and federal incentives without state electric vehicle rebates. The first three
columns of the exhibit list each vehicle and provide its electric battery range and technology
type. Column 4 provides the final estimates of California EV sales by nameplate, taken from
Column 10 of Exhibit 4-2. Column 5 provides the elasticity estimate, which the research team
developed and this report discusses in Chapter 2. Column 6 provides the number of units of lost
sales and federal incentives without state electric vehicle rebates, which the spreadsheet
calculates by multiplying Column 4 by Column 5.
Column 7 provides the lost Federal tax credits without state electric vehicle rebates, which the
spreadsheet calculates by multiplying Column 5 by Column 6 of Exhibit 4-3.
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Exhibit 4-4: 2013 Lost Sales and Rebates without State Electric Vehicle Incentives
EV Vehicle
Type
Vehicle Model Range
Chevy Volt
38 PHEV-40
Nissan Leaf
75 BEV-100
Tesla Model S
208-265 BEV-100
Toyota PiP
11 PHEV-20
Ford CX-Max Energi
21 PHEV-20
Ford Fusion Energi
21 PHEV-20
Ford Focus Electric
76 PHEV-100
Toyota RAV4 EV
103 BEV-100
Mitsubishi I-MiEV
62 PHEV-100
Smart ED
87 BEV-100
Fiat 500e
87 BEV-100
Honda Fit EV
82 BEV-100
Chevrolet Spark EV
82 BEV-100
Honda Accord PHV
13 PHEV-20
Porshe PanameraS-E
N.A PHEV-20
Cadillac ELR
N.A PHEV-40
Total
2013
California
Lost Unit Sales
PHEV/BEV Elasticity Without State
Unit Sales Estimate
Rebate
9,529
3.6
1,338
10,562
3.6
3,003
8,347
3.6
990
4,987
3.6
817
2,952
3.6
440
2,512
3.6
319
812
3.6
189
512
3.6
84
481
3.6
135
431
3.6
141
301
3.6
76
266
3.6
58
252
3.6
77
217
3.6
27
35
3.6
2
2
3.6
0
42,198
7,697
Lost Federal
Incentives
Without State
Rebate
10,034,963
22,525,743
7,423,472
2,043,052
1,650,768
1,196,266
1,416,701
631,469
1,013,726
1,059,334
569,439
436,353
579,552
100,534
6,605
600
50,688,576
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4.4 Forecasts for 2015 to 2019
This study was able to consider the actual registrations of EVs in California though 2013 (See
Exhibit 4-1). In contrast, the developers of the IVC started with a rough estimate of 2011 sales, a
CEC forecast for 2015, and a 2030 penetration consistent with ZEV regulations. Exhibit 4-5
provides the sales estimates from the IVC for the PEV15 scenario (blue line), the PEV45
scenario (redline), and actual sales with trend line (green line). As shown realized sales through
2013 have far exceeded the sales that the IVC predicted. However, the IVC estimates for the
PEV45 scenario closely match the trend line for realized sales for the period 2015 through 2018.
As a result, this study used the realized sales and trend line for the period through 2014 and the
IVC’s PEV45 scenario for the period 2015 through 2019.
Exhibit 4-5: California Electric Vehicle Sales and Forecasts
180,000
160,000
140,000
120,000
100,000
2014 Thru Jun = 28,187
2013 thru Jun = 14,427
80,000
60,000
40,000
20,000
0
2011
2012
2013
Scenario A
2014
2015
Scenario B
2016
2017
2018
2019
Actual/Trendline
Exhibit 4-6 provides the portion of the study spreadsheet that forecasts the value of several key
variables for the 5-year period 2015 through 2019. The spreadsheet estimates the values of each
of these variables in each of the forecast years by scaling in proportion to the increase in unit
sales
Exhibit 4-6: Forecasts of Key Variables for 2015-19
Year
2015
2016
2017
2018
2019
2015-2019 Total
2015-2019 Average
2013
2013
California
California
PHEV/BEV 2013 Price PHEV/BEV Sales
Unit Sales
2013 $
(MSRP)
63,200
39,713
2,509,880,193
82,103
39,713
3,260,595,025
96,180
39,713
3,819,607,320
119,938
39,713
4,763,142,568
158,577
39,713
6,297,601,816
519,998
39,713 20,650,826,922
104,000
39,713
4,130,165,384
Federal
Incentive California Rebate
404,531,145
97,047,451
525,527,968
126,074,717
615,627,043
147,689,580
767,701,789
184,172,473
1,015,018,995
243,504,133
3,328,406,940
798,488,355
665,681,388
159,697,671
State Sales
Taxes @7.5%
188,241,014
244,544,627
286,470,549
357,235,693
472,320,136
1,548,812,019
309,762,404
Lost Unit Sales
Local Sales Without State
Rebate
Taxes @1.25%
31,373,502
11,528
40,757,438
14,976
47,745,092
17,544
59,539,282
21,878
78,720,023
28,926
258,135,337
94,852
51,627,067
18,970
Lost Federal
Incentives
Without State
Rebate
75,916,347
98,623,218
115,531,663
144,070,774
190,483,563
624,625,565
124,925,113
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o
5. The IMPLAN Model and Model Runs
Economic impact studies track the movement of money through an economy and measure the
cumulative effects of that spending. Because the financial and employment impacts of proposals
and projects are used in making decisions on policies, investments, grants and other assistance
programs, these studies have wide spread use and appeal. The IMPLAN System is a general
input-output modeling software and data system that tracks every unique industry group in every
level of the regional data, and almost all of the data elements are available for customization.
Sources for creation of the background IMPLAN data include BLS, BEA, and Census.
IMPLAN traces local impacts by looking back through the supply chain. These backward
linkages provide IMPLAN with the information to model the iterations of local Indirect and
Induced impacts until the initial spending completely escapes from the study area by leakage.
While IMPLAN's ability to examine economic impacts is virtually without limits, the software
cannot predict the future or estimate impacts that do not have direct fiscal, production or
employment elements.
The research team used IMPLAN to model the economic and fiscal impacts of California electric
vehicle purchases as well as the lost sales and rebates without state electric vehicle incentives.
Section 5.1 provides an overview of the IMPLAN model and the benefits of using the IMPLAN
model for this analysis. Section 5.2 describes general considerations in the stimulation of events
in the INPLAN model such as sector selection, local purchase percentages, and margins. Section
5.3 describes how the IMPLAN model was stimulated for this study using the out puts of the
IVC and the Market and Elasticity Spreadsheet. Section 5.4 describes the model results and
calibration of those results to the earlier studies by U. C. Berkeley that used the BEAR model.
5.1 Overview and Benefits of the IMPLAN Model
The US Department of Agriculture developed the original IMPLAN software and data in 1976.
The model has migrated to the University of Minnesota and to the Minnesota IMPLAN Group, a
private-sector company. The model developers have continuously updated the model with the
current model based on 2012 data. The model has a combined 35 years of experience and
thousands of national and regional economic studies have used IMPLAN.
IMPLAN is an acronym for IMpact analysis for PLANning. The IMPLAN System is a general
input output model that is comprised of software and regional data sets. IMPLAN allows users of
varying skill levels and backgrounds in economics to create cost effective, accurate economic
reports. The software's ability to perform sophisticated economic calculations in a matter of
minutes both streamlines, and standardizes the process that formerly took expert economists
weeks to accomplish.
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One of the most powerful aspects of IMPLAN is that it can create input-output models for
specific regional economies. Rather than extrapolating regional data from national averages,
IMPLAN measures economic impacts from data representing actual local economies. IMFLAN
data sets are available from the ZIP Code level to the national level, and regional files can be
combined to create precise geographic definitions when ca1culaling impacts. This study uses the
California state-level package.
The analysis results provide the IMPLAN user or client with a report that demonstrates the
detailed effects of local changes on supporting industries and households. Reports can provide
both detailed and summary information related to job creation, income, production, and taxes.
IMPLAN Version 3.0 can even track the impacts of a local change on surrounding economies.
IMPLAN data tracks all the available industry groups in every level of the regional data. This
permits detailed impact breakdowns and helps ensure accuracy of inter-industry relationships. If
a study involves the introduction of an industry group that does not already exist in the local
area, IMPLAN provides tools to create a new industry. The IMPLAN user can apply this new
industry as a proxy to estimate the likely impacts of the new industry's production to the local
economy. If an industry exists in IMPLAN, but does not exactly match the sales and
employment information for the industry the user is modeling, the user may update IMPLAN
industry relationships to match the known values, while still maintaining the local regional sales
and employment averages for examining the Indirect and Induced impacts.
Exhibit 5-1 provides a timeline highlighting some of the key dates and accomplishments of the
IMPLAN model. For example, more than 260 colleges and universities to estimate their
economic impacts on local communities have used IMPLAN. 10 The first California state
government department begins using IMPLAN in 1991.
10
IMPLAN website, “Clients.” http://www.implan.com/V4/index.php?option=com_content&view=article&id=64&Itemid=25
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Exhibit 5-1: IMPLAN Timeline
1976 • The USDA Forest Service develops an economic impact modeling system
1977 • USDA named the system IMPLAN (IMpact analysis for PLANning)
1984 • USDA partners with the University of Minnesota to expand and update IMPLAN data
1988 • IMPLAN is first used outside the federal government
1991 • The first California state government department begins using IMPLAN
1993 • MIG (Minnesota IMPLAN Group) becomes a private-sector company
1993 • St. Paul, Minnesota becomes the first city to buy IMPLAN
1993 • The Federal Emergency Management Agency (FEMA) starts using IMPLAN
1993 • CH2M Hill buys IMPLAN for the first time
1994 • UNC Charlotte buys IMPLAN for the first time
1994 • Duke Power starts using IMPLAN
1995 • The Bureau of Reclamation becomes an IMPLAN user.
1996 • Held the first semiannual national IMPLAN conference
1996 • The Bureau of Economic Analysis (BEA) becomes an IMPLAN customer.
1999 • Released version 2.0 of the IMPLAN software, expanding Social Accounting Matrices
1999 • The Bureau of Land Management (BLM) becomes an IMPLAN customer
1999 • Ernst & Young starts using IMPLAN
2000 • The Federal Reserve Bank starts using IMPLAN
2001 • Booz Allen Hamilton becomes an IMPLAN customer
2001 • The Environmental Protection Agency (EPA) starts using IMPLAN
2009 • IMPLAN designated as acceptable way to track new job growth for ARRA program
The research team has used IMPLAN and other input-output models independently as well as in
combination for numerous projects. The research team is very familiar with input-output models
such as IMPLAN RIMS and REMI. The research team has completed more than 250 economic
impact studies using these models for businesses, nonprofit organizations, and federal, state, and
local government agencies.
While there are a number of different options of input-output models, the research team selected
IMPLAN as the best option for this economic impact study, given the software’s wide
acceptance, versatile functionality, and the ease of interpretation of its results. Exhibit 5-2
provides a comparison of IMPLAN and other commonly used input-output models, such as the
U.S. Department of Commerce RIMS II model (RIMS II) and REMI.
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Exhibit 5-2: Comparison of IMPLAN and Other Input-Output Models
IMPLAN has advantages over the more simplistic RIMS II input-output model, which
applies a small set of multipliers, relative to the number of multipliers available in
IMPLAN, to the various economic activities. Another advantage IMPLAN has over
the RIMS II is that IMPLAN automatically divides impacts into the traditional
economic impact analysis subcategories: direct, indirect, and induced effects. RIMS II
is a spreadsheet-based model where the user is responsible for setting up the multiplier
worksheet. Each time the user adds a new variable the user must alter the worksheet,
increasing the chance of user-induced error. RIMS II also adds additional ordering and
processing time to the analysis, which can extend the duration of a study.
IMPLAN has advantages over the REMI model because IMPLAN has a simpler data
entry interface. The REMI model is approximately 20 times more expensive and a
REMI model analysis can be several times more expensive than an IMPLAN model
analysis, depending on the complexity of the modeling effort.
In summary, IMPLAN is a more sophisticated and less user-error prone tool than
RIMS II, and a significantly more user-friendly and economical tool than REMI.
One specific advantage of the IMPLAN model for this analysis is the Social Accounting
Matrices (SAM). The IMPLAN model uses SAM to trace the effects of economic transactions
undertaken by an institution, business, or agency in a given year. The model generates detailed
results that can be sorted by impact type (employment, output, labor income, and value added),
and a tax impact report that estimates changes to federal, state, and local government tax
revenues.
MIG Inc. constructs IMPLAN data sets annually. MIG derives the regional data from many
different sources, primarily federal agencies responsible for data collection. The primary sources
for data derivation include:
•
•
•
•
•
•
•
•
•
The U.S. Bureau of Labor Statistics (BLS) Covered Employment and Wages (CEW)
program
The U.S. Bureau of Economic Analysis (BEA) Regional Economic Information System
(REA) program
The U.S. Bureau of Economic Analysis Benchmark I-O Accounts of the US
The BEA output estimates
The BLS Consumer Expenditure Survey
The U.S. Census Bureau County Business Patterns (CBP) program
The U.S. Census Bureau Decennial Census and Population Surveys
The U.S. Census Bureau Economic Censuses and Surveys
The U.S. Department of Agriculture Census
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When combined, these sources provide all the elements needed to assemble a complete U.S. data
set. However, the actual assembly of these elements, into a cohesive and complete U.S.
IMPLAN data set formatted for the software, requires about five months. Since BLS does not
release the CEW data until July of the following year (i.e. BLS released 2008 data in July of
2009), MIG also releases IMPLAN data sets one year after the current calendar year, typically in
the month of December.
5.2 General IMPLAN Modeling Considerations
In IMPLAN, “Events” describe the actual change in production or purchases that the user is
modeling. Therefore, there is one event for each production change or purchase. As described in
the previous two chapters, the purchase and use of electric vehicles in California will cause a
number of events to occur including increased in the cost of vehicle purchases, decreases in
purchases of gasoline, increases in purchases of electricity, payments of rebates by government
entities, and increases in non-vehicle related purchases by consumers. For each event, the
IMPLAN user must select the appropriate, sector and set parameters that include the Local
Purchase Percentage (LPP) and margins for transportation, wholesale and retail sectors. The
following subsections describe the considerations that the IMPLAN user considers when making
these selections.
Sector Selection
When modeling an event, the first selection that an INPLAN user needs to make is the selection
of the appropriate industry sector. IMPLAN lists sectors in ascending North American Industry
Classification (NAICS) code order, with a few exceptions. Variance occurs because some
IMPLAN Sectors do not have NAICS correspondence. Most notably among these are the
Construction Sectors, which IMPLAN organizes on Census structure types, and the State and
Local Government and Federal Government Enterprise and Payroll Sectors.
Proper sector selection is a key to getting accurate results, because the sector selection dictates
both the, output to employment and output to labor income factors. The sector also determines
the industry purchases and purchasing ratios, as well as, the ratio of commodity purchases to
labor and other value added payments.
Local Purchase Percentages
The Local Purchase Percentage (LPP) determines the proportion of an event’s sales that local
production satisfies and that IMPLAN should apply to the multipliers. Typically, a user will
source the entire sales or employment value of their event within the Study Area, because the
location of the economic change is a key feature in determining Study Area selection. Therefore,
by default, LPP in IMPLAN is always 100 percent. This is the case for most changes in industry
production, like local expansion of a factory, a new product line added to a plant, a new
corporation moving into an area, or in most cases local construction.
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However, an LPP of 100 percent may not be a safe assumption when examining impacts of
purchases. When a consumer or a government institution makes a purchase, it is likely that
purchasers will source only a portion of their purchase from a local vendor. IMPLAN provides
means for both setting to a known LPP value or to the Study Area ratio for the purchase of
locally available commodities (SAM Model Value). When a user applies an LPP of less than 100
percent, the assumption is that the remainder of the purchase/production value falls to producers
outside of the region creating demand for imports.
It is important to note that LPP applies only to direct sales and not to sales and employment
generated through the backward linkages. IMPLAN calculates the effects of backward linkages
using Regional Purchase Coefficients (RPCs). RPCs apply to indirect and induced effects,
reflecting the loss of sales to regions outside the study area to import goods and services that are
required, but that local firms do not produce.
Margins
Margins convert retail sales costs, the costs of the goods and services that purchasers buy in a
retail environment, back to producer costs. Correct application of margins to retail sales values
is important because IMPLAN bases all the underlying relationships on the values of production
for a specific industry sector. Margins can have dramatic impacts on retail sales values entered
into IMPLAN, because consumers rarely purchase products directly from the producer. Instead,
products purchased by households typically move from factories to wholesale distribution
centers and from wholesalers to retailers. At each stage of this process, transportation, wholesale
and retail entities add additional fees to the original value of the product, summing to the cost
paid at a retail store.
Because IMPLAN accounting relationships assume that all entered values are at production
costs, it is critical that modelers’ margin retail purchases to capture their true production values.
If users do not margin retail sales values, scenario results will be overstated, because costs
unrelated to production (e.g. the retail markup, wholesale markup, and transportation) will be
attributed to the value of production of the producing industry. Hence, not only will IMPLAN
overestimate the direct impact, but IMPLAN will inflate the Indirect and induced results as well.
There are two different methods for applying margins to retail or wholesale purchases in the
IMPLAN Model. When the user knows the items purchased at retail value, the user should assess
margins to the producing industry's sector. If the user only knows the total bill of goods than user
must attribute the sales value to the appropriate retail sector. Margins only apply to commodities
not purchased directly from the producing industry. Because of this, some sectors are not
available for margining because they are services purchased directly from the producer, such as a
haircut from a stylist, or because the commodity that a sector produces is considered only an
inter-industry commodity such as medicinal and botanical pharmaceutical preparations. The
IMPLAN software has built-in margins for all marginable sectors for easy conversion between
retail sales and producer values.
5.3 Stimulation of the IMPLAN Model
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Exhibit 5-3 lists each of the events that the research team modeled using the IMPLAN model.
The exhibit lists the names of the events or variables (i.e. Elec.ZEV45) which, with a few
exceptions, are those that the U.C. Berkeley researchers used from the original IVC. The exhibit
also provides the IMPLAN settings for each event including the sector, the margin settings and
the Local Purchase Percentage settings. The exhibit also provides the dollar values (in millions)
for each event for each of the modeled years, 2015-2019. The following subsections describe
each of the model settings including a general description of the concept and a rational for the
particular value chosen for each event.
Exhibit 5-3: Events Modeled with IMPLAN
Variable
IVC Non-Tesla
Tesla IVC
Tesla Non-IVC
Fuel.ZEV45
Description Incremental purchase Incremental
Non-incremental
Reductions in gasoline
costs for non-Tesla EVs purchase costs for purchase costs for Tesla consumption due to shift
Tesla EVs
EVs with margins
to EVs
removed
IMPLAN
Sector
Margined
Local
Purchase
Percentage
(LPP)
2015
2016
2017
2018
2019
Elec.ZEV45
Increase in
electricity
consumption due
to shift to EVs
Net.ZEV45
Net increase in consumer
spending taking into account
incremental vehicle costs,
increased gasoline and
decreased electricity
purchases, tax credits and
rebates.
Incent.ZEV45
Decrease in general state
government spending due to
EV rebate spending
276
276
276
115
31
PCE (Median, 50-75k)
PCE (Median, 50-75k)
Yes
Yes
No
No
N.A.
N.A.
SAM Value (6.51%)
Set to 100%
Set to 100%
Yes (Custom)
Petroleum Refining =
70.75 %
Wholesale Trade =5.75
%
Retail Trade
=21.01 %
SAM Value (80.46%)
Set to 70%
(Custom)
N.A.
N.A.
466.175
544.102
563.006
612.464
701.936
244.468
285.335
295.248
321.185
368.105
428.812
579.418
705.780
912.684
1245.886
85.787
126.333
174.575
235.878
318.686
211.146
273.698
385.865
635.099
1035.653
-97.025
-126.045
-147.655
-184.129
-243.447
IMPLAN Inputs ($, Million)
-288.349
-413.341
-565.976
-749.038
-1002.319
IVC.Non-Tesla
The incremental vehicle cost (IVC.ZEV45) is a variable that the IVC model calculates and that
the U.C. Berkeley researchers used as an input to the BEAR model. The IVC represents the
incremental cost of purchasing an electric vehicle that consumers recoup to some extent through
rebates, incentives and lower fuel costs. For this study, the research team made two adjustments
to the incremental vehicle cost. First, the IVC in this study represents the unfinanced incremental
cost whereas the U.C. Berkeley study financed these payments over five years. This study retains
the financing for the consumer variable. However, this analysis assumes the impact on the
vehicle-producing sector occurs in the year the consumer purchases the vehicle. Second, the
analysis has split the incremental vehicle cost between the incremental vehicle costs associated
with Tesla vehicles and the remaining incremental vehicle costs. This split allows analysis within
the IMPLAN model to employ a 100 percent LPP for the Tesla incremental vehicle cost as
Tesla’s manufacturing facilities are in California and the 6.51 percent IMPLAN model percent
LPP for the non-Tesla incremental vehicle cost. The remaining manufacturers and dealers also
conduct business in California that is directly related to electric vehicles, e.g. engineering,
marketing and sales, but separating out these components as they relate to electric vehicles was
beyond the scope of this project. The Tesla operations are relatively simple to analyze as Tesla
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produces only electric vehicles. The analysis assigned the portion of the IVC associated with
vehicles that manufacturers other than Tesla produce (IVC.Non-Tesla) to IMPLAN Sector 276,
margined the value, and set the Local Purchase Percentage (LPP) to the SAM Value of 6.51
percent.
The research team assigned this event to IMPLAN Sector 276, Automobile Manufacturing, as
this IMPLAN sector produces automobiles. Note that IMPLAN has a sector 320, Retail-Motor
Vehicles and Parts, however this is a margin only sector and as a result does not buy
automobiles, but only purchases the inputs required to retail that product to the public.
The research team margined this purchase, as automobile purchasers must pay margins to the
transport, wholesale and retail sectors. For the Local Purchase Percentage (LPP), the research
team assigned value of zero percent as no automaker other than Tesla produces automobiles in
California.
Tesla. IVC
The second event that the research team modeled was the remaining incremental cost associated
with Tesla purchases (Tesla.IVC). As discussed above, the incremental vehicle cost
(IVC.ZEV45) is a variable that the IVC model calculates and that the U.C. Berkeley researchers
used as an input to the BEAR model. For this study, the research team has split the incremental
vehicle cost between the incremental vehicle costs associated with Tesla vehicles and the
remaining incremental vehicle costs. This split allows the analysis within IMPLAN to employ a
low 6.51 percent LPP. As with the (IVC.Non-Tesla), the analysis assigned the event to IMPLAN
Sector 276, Automobile Manufacturing and margined the value.
Tesla.Non-IVC
The third event that the research team modeled was the dollar value of Tesla purchases excluding
incremental vehicle costs (Tesla.Non-IVC). The Market Share/Elasticity Spreadsheet Model
calculates the value EV sales and the analysis assumes Tesla production retains the same
percentage of the market as in 2013. The analysis subtracts the Tesla IVC from this amount. The
analysis also subtracts out margins as the margin sectors are unaffected by which manufacturer
produces the vehicle and where. The research team multiplied the event value by 69.28 percent,
which is the share of margined Sector 276 purchases that IMPLAN assigns to manufacturers.
The research team assigned this event to IMPLAN Sector 276, Automobile Manufacturing, as
this IMPLAN sector produces automobiles. The research team did not margin this purchase, as
the analysis eliminated the margins prior to entering the event into the model. For the Local
Purchase Percentage (LPP), the research team assigned value of 100 percent as Tesla produces
all of its automobiles in California.
Fuel.ZEV45
The fourth event that the research team modeled was the dollar value in gasoline fuel savings for
consumers purchasing electric vehicles (Fuel.ZEV45). The IVC calculates this value. The
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research team assigned this event to IMPLAN Sector 115, Petroleum Refineries, as this
IMPLAN sector produces gasoline. Note that IMPLAN has a sector 20, Oil and Gas Extraction,
but this sector produces crude oil, which is an input to the Petroleum Refining Sector. The Oil
and Gas Extraction Sector does not produce finished gasoline. In addition, IMPLAN has a sector
326, Retail-Gasoline Stations, however this is a margin only sector and as a result does not buy
gasoline, but only purchases the inputs required to retail that product to the public.
The research team margined this purchase, as gasoline purchasers must pay margins to the
transport, wholesale and retail sectors. The IMPLAN model has built in margins that reflect all
of the purchases from the gasoline stations including convenience store type items. Therefore,
the research team developed gasoline specific margins and applied them to IMPLAN. The
analysis left the transportation margins at their original level and set the petroleum-refining
margin to 70.75 percent, the wholesale trade margin to 5.75 percent and the retail margin to
21.01 percent. These margin estimates are from the California Energy Commission (CEC),
which publishes data on estimated gross margins for both refiners and distributors. Margins
include both the cost and profit added at different stages of the supply chain. For this study, the
research team used the margins reported by CEC in the 2014 Energy Almanac for branded
retailers combining them into three margin categories, petroleum refining, wholesale trade and
retail trade.
For the Local Purchase Percentage (LPP), the research team assigned the SAM value of 80.46
percent. As described above, the default LPP in IMPLAN is 100 percent as most economic
impact studies examine changes in industry production, like local expansion of a factory, a new
product line added to a plant, or a new corporation moving into an area. However, an LPP of 100
percent is not the correct assumption when examining impacts of purchases as it is likely that
purchasers will source only a portion of their purchase from a local vendor. IMPLAN provides
means for setting LPP to the Study Area ratio for the purchase of locally available commodities
(SAM Model Value).
Elec.ZEV45
The fifth event that the research team modeled was the additional dollar value of electricity
purchases for consumers of electric vehicles (Elec.ZEV45). The IVC calculates this value. The
research team assigned this event to IMPLAN Sector 31, Electric Power Generation,
Transmission and Distribution, as this IMPLAN sector both produces and distributes electricity.
The user cannot margin sector 31 as it sells directly to the consumer
For the Local Purchase Percentage (LPP), the research team assigned a value of 70 percent. As
described above, the default LPP in IMPLAN is 100 percent as most economic impact studies
examine changes in industry production, like local expansion of a factory, a new product line
added to a plant, or a new corporation moving into an area. However, an LPP of 100 percent is
not the correct assumption when examining impacts of purchases as it is likely that purchasers
will source only a portion of their purchase from a local vendor. IMPLAN provides means for
both setting to a known LPP value or to the Study Area ratio for the purchase of locally available
commodities (SAM Model Value). In this case, the research team relied on specific data on the
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percentage of electricity that California produces. According to the latest available data from the
California Energy Commission’s, Energy Almanac, in 2011, California produced 70 percent of
the electricity it uses.
Net.ZEV45
The sixth event that the research team modeled was the net additional dollars that be available
for consumers to spend. Consumers of electric vehicles will spend more on these electric
vehicles, less on gasoline, more on electricity, and will receive tax credits and rebates.
Therefore, the IVC calculates that their net spending (personal consumption expenditures) for all
goods will increase. The consumer analysis retains the original IVC assumption that consumers
will finance spending on electric vehicles by adding an additional five percent to the purchase
cost and then spreading the cost over five years.
The analysis assigns the purchases to personal consumption expenditures (PCE) for households.
For simplicity, the analysis assumes households with the median income for Californians of
$50,000 to 75,000 make all purchases. The IMPLAN model already applies margins to all PCE
purchases.
For the Local Purchase Percentage (LPP), the research team assigned a value of 100 percent.
This indicates that all of the incentive payouts and net automotive/fuel spending decreases accrue
to households local to California. It does not mean that these households make all their purchases
in the state. Determining what households purchase from local producers is the function of
IMPLAN’s trade flow RPC values for the study area.
Incent.ZEV45
The seventh and final event that the research team modeled was the dollar value of incentives
spent by the state government (Incent.ZEV45). The IVC calculates this value.
The research team examined three methods for how to model this event. One option was to omit
the event, as it is the cost of the program not an outcome. In a benefit-cost analysis, this spending
would represent the cost of the program and would represent the denominator in the benefit-cost
ratio. Another option was to assume that in the absence of the program, consumers would no
longer get the incentive so the government would no longer need the income and would lower
taxes. In this instance, the analysis treats this variable in exactly the same manner as the
Net.ZEV45 variable above, but with a negative sign. The third and final option was to assume
that the funds came at the expense of general government spending and that the analysis could
stimulate the IMPLAN model with negative government spending.
The research team elected to model the first two options. For the third option, the only
alternative was for the analysis to assume that general state government spending (excluding
education) declined across the board. This did not appear to be a logical assumption.
LCFS.ZEV45
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The IVC included an additional variable that represented the dollar value of LCFS payments
spent by the state government (LCFS.ZEV45). The IVC calculates this value which has a small
value relative to the other events considered above. The research team did not model this event.
If consumers purchase electric vehicles, the government pays and consumers receive funds that
reflect the cost of reducing green house gases (GHG). If consumers do not purchase the electric
vehicles, the government does not pay a credit and consumers do not receive funds. In both
instances, the parties receive compensation.
5.3 IMPLAN Model Results
The research team ran IMPLAN for each of the events described in the previous section for the
year 2015. The spreadsheet calculates values for the additional years 2016 through 2019 by
scaling the results in proportion to the value of the event in comparison to the 2015 value. As
IMPLAN is a linear model, the results are identical to stimulating the model with the value in the
later years. Exhibit 5-4 presents the results the IMPLAN results for each event and in total for
each year. The following chapter discusses the implications of these results.
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Exhibit 5-4: IMPLAN Model Results
Year
IVC Non-Tesla
Tesla IVC
Tesla Non-IVC
2015
2016
2017
2018
2019
1,862
2,174
2,249
2,447
2,804
1,705
1,990
2,059
2,240
2,567
1,594
2,154
2,624
3,394
4,632
2015
2016
2017
2018
2019
187
218
226
246
281
178
208
215
234
268
176
238
290
375
513
2015
2016
2017
2018
2019
291
339
351
382
438
439
513
531
577
662
736
995
1,212
1,568
2,140
2015
2016
2017
2018
2019
29
34
35
38
43
22
25
26
28
33
14
19
23
30
41
Fuel.ZEV45
Elec.ZEV45
Employment
-1,114
226
-1,598
333
-2,187
461
-2,895
622
-3,874
841
Value Added
-206
57
-296
84
-405
117
-536
158
-717
213
Output
-356
88
-510
130
-699
179
-925
242
-1,237
327
Indirect Business Taxes (IBT)
-22
12
-32
17
-44
23
-58
32
-78
43
Net.ZEV45
Incent.ZEV45
Total
1,955
2,535
3,573
5,881
9,590
-898
-1,167
-1,367
-1,705
-2,254
5,331
6,421
7,412
9,984
14,307
197
255
360
592
966
-90
-118
-138
-172
-227
499
591
665
897
1,296
312
405
571
940
1,533
-144
-187
-218
-272
-360
1,368
1,686
1,927
2,512
3,502
17
23
32
53
86
-8
-10
-12
-15
-20
63
75
83
107
147
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Feburary 2014
6. Results and Conclusions
The revised IVC model and the new IMPLAN based models produce estimates of impacts on the
California economy for the current CVRP program and an alternative scenario where the CVRP
is suspended and the Federal Rebate program continues. The research team ran the IMPLAN
model annually for a 5-year period, 2015-2019. This allows the research team to point to single
year values, to an average of the next 5 years or to the total impact over the next five years. The
California EV market is growing rapidly and a 5-year analysis capture this trend.
For 2015, some key estimates and results for the current CVRP include:
•
•
•
•
•
•
•
•
•
•
•
•
New PHEV & BEV Sales - 63,200
Value of Sales - $2.5 billion
Federal Incentives Earned - $405 million
State Incentives Paid - $97 million (this actual payout is only about 76 percent of earned)
Reduced Gasoline Consumption – 73 million gallons
Reduced Gasoline Consumption – $300 million saved at current regular fuel price ($4.13)
Reduced GHG Emissions – 132,034 MTCO2e
Reduced GHG Emissions - $4.3 million (@ $32.50 MTCO2 reduced)
Jobs Created - 5,331
Indirect Business Taxes (IBT) Received - $63 Million (IBT are essentially sales, excise
and other fees paid)
Value Added, a measure of GSP - $499 Million
Output - $1.4 Billion
For 2015, the projected losses in these values that might result from California suspending the
CVRP include:
•
•
•
•
•
•
•
•
•
•
•
•
Lost New PHEV & BEV Sales - 11,528
Lost Value of EV Sales - $458 Million
Lost Federal Incentives Earned - $74 Million
State Rebates Paid – Saves $97 million
Increased Gasoline Consumption – 13 million gallons
Increased Gasoline Consumption – $54 million saved at current regular fuel price ($4.13)
Increased GHG Emissions – 24,084 MTCO2e
Increased GHG Emissions - $780 thousand (@ $32.50 MTCO2 reduced)
Jobs Lost - 972
Lost Indirect Business Taxes (IBT) Received - $11 Million
Lost Value Added, a measure of GSP - $91 Million
Lost Output – $249 Million
38
California Electric Transportation ROI Assessment
Feburary 2014
The Federal tax credit continues to support PHEV and BEV sales that produce California jobs
and GSP, but losing the CVRP incentive, has a substantial impact on the positive impacts of
larger EV sales on the economy as well as additional gasoline consumption and GHG emissions.
Exhibit 6-1 provides these values for the full period 2015-2019. A major difference between the
U.C. Berkeley analysis and that included here is the recognition of the value of Tesla in-state
vehicle production. The BEAR model did not consider the added impacts on the California
economy of returning vehicle production and assembly to California. The auto assembly
industry was a vital contributor to the California economy and could be once again.
Exhibit 6-1: Impact of CVRP on General Fund Revenues and Economic Activity
Net Impact Including Change in State Government Spending
(Millions of 2014 Dollars)
New PHEV and BEV Sales (Units)
Value of Sales at MSRP
Federal Incentives Earned
State Rebates
State Vehicle Sales Tax Paid on EVs (7.5%)
Local Vehicle Sales Tax Paid on EVs (1.75%)
Petroleum Demand Reduction (Million Gallons)
GHG Emissions Reductions (MTCO2)
Employment Impacts (Jobs)
Value Added (Approx GSP)
Output
Indirect Business Taxes
New PHEV and BEV Sales (Units)
Value of Sales at MSRP
Federal Incentives Earned
State Rebates
State Vehicle Sales Tax Paid on EVs (7.5%)
Local Vehicle Sales Tax Paid on EVs (1.75%)
Petroleum Demand Reduction (Million Gallons)
GHG Emissions Reductions (MTCO2)
Employment Impacts (Jobs)
Value Added (Approx GSP)
Output
Indirect Business Taxes
New PHEV and BEV Sales (Units)
Value of Sales at MSRP
Federal Incentives Earned
State Rebates
State Vehicle Sales Tax Paid on EVs (7.5%)
Local Vehicle Sales Tax Paid on EVs (1.75%)
Petroleum Demand Reduction (Million Gallons)
GHG Emissions Reductions (MTCO2)
Employment Impacts (Jobs)
Value Added (Approx GSP)
Output
Indirect Business Taxes
2015
2016
2017
2018
Under Current CVRP Rules - Base Case (Based on Roland-Holst HEV45)
63,200
82,103
96,180
119,938
2,510
3,261
3,820
4,763
405
526
616
768
97
126
148
184
188
245
286
357
31
41
48
60
73
104
140
184
132,034
195,726
271,065
367,065
5,331
6,421
7,412
9,984
499
591
665
897
1,368
1,686
1,927
2,512
63
75
83
107
With Elimination of the CVRP Incentives - Option 1
51,672
67,127
78,636
98,061
2,052
2,666
3,123
3,894
331
430
503
628
154
200
234
292
26
33
39
49
60
85
114
150
107,950
160,024
221,621
300,109
4,358
5,250
6,060
8,163
408
483
544
733
1,118
1,378
1,576
2,054
51
61
68
87
Negative Change Between Base Case and Option 1
11,528
14,976
17,544
21,878
458
595
697
869
74
96
112
140
97
126
148
184
34
45
52
65
6
7
9
11
13
19
25
34
24,084
35,702
49,445
66,956
972
1,171
1,352
1,821
91
108
121
164
249
307
352
458
11
14
15
19
2019
5-Year Total
Annual Average
158,577
6,298
1,015
244
472
79
242
497,168
14,307
1,296
3,502
147
519,998
20,651
3,328
798
1,549
258
742
1,463,058
43,455
3,948
10,994
475
104,000
4,130
666
160
310
52
148
292,612
8,691
790
2,199
95
129,651
5,149
830
386
64
198
406,480
11,697
1,060
2,863
120
425,146
16,884
2,721
1,266
211
607
1,196,184
35,528
3,228
8,989
388
85,029
3,377
544
253
42
121
239,237
7,106
646
1,798
78
28,926
1,149
185
244
86
14
44
90,687
2,610
236
639
27
94,852
3,767
607
798
283
47
135
266,874
7,927
720
2,005
87
18,970
753
121
160
57
9
27
53,375
1,585
144
401
17
This analysis estimates the Tesla in-state production impacts using IMPLAN's ICE production
sector as IMPLAN, like BEAR, does not have an EV production sector. Developing a California
EV production vector, particularly one that could capture EV-specific jobs created by all
manufacturers, and adding it to IMPLAN could improve the accuracy of the estimated impacts.
However, the estimation of a California EV production vector was beyond the scope of this
research effort.
39
California Electric Transportation ROI Assessment
Feburary 2014
Exhibit 6-2 presents a similar set of results except that it does not consider the opportunity costs
of the rebate funds. It provides the full gross impact of the rebate expenditure.
Exhibit 6-2: Impact of CVRP on General Fund Revenues and Economic Activity
Gross Impact Not Including Change in State Government Spending
(Millions of 2014 Dollars)
New PHEV and BEV Sales (Units)
Value of Sales at MSRP
Federal Incentives Earned
State Rebates
State Vehicle Sales Tax Paid on EVs (7.5%)
Local Vehicle Sales Tax Paid on EVs (1.75%)
Petroleum Demand Reduction (Million Gallons)
GHG Emissions Reductions (MTCO2)
Employment Impacts (Jobs)
Value Added (Approx GSP)
Output
Indirect Business Taxes
New PHEV and BEV Sales (Units)
Value of Sales at MSRP
Federal Incentives Earned
State Rebates
State Vehicle Sales Tax Paid on EVs (7.5%)
Local Vehicle Sales Tax Paid on EVs (1.75%)
Petroleum Demand Reduction (Million Gallons)
GHG Emissions Reductions (MTCO2)
Employment Impacts (Jobs)
Value Added (Approx GSP)
Output
Indirect Business Taxes
New PHEV and BEV Sales (Units)
Value of Sales at MSRP
Federal Incentives Earned
State Rebates
State Vehicle Sales Tax Paid on EVs (7.5%)
Local Vehicle Sales Tax Paid on EVs (1.75%)
Petroleum Demand Reduction (Million Gallons)
GHG Emissions Reductions (MTCO2)
Employment Impacts (Jobs)
Value Added (Approx GSP)
Output
Indirect Business Taxes
2015
2016
2017
2018
Under Current CVRP Rules - Base Case (Based on Roland-Holst HEV45)
63,200
82,103
96,180
119,938
2,510
3,261
3,820
4,763
405
526
616
768
97
126
148
184
188
245
286
357
31
41
48
60
73
104
140
184
132,034
195,726
271,065
367,065
6,229
7,589
8,779
11,689
589
708
803
1,069
1,511
1,872
2,146
2,784
71
85
95
122
With Elimination of the CVRP Incentives - Option 1
51,672
67,127
78,636
98,061
2,052
2,666
3,123
3,894
331
430
503
628
154
200
234
292
26
33
39
49
60
85
114
150
107,950
160,024
221,621
300,109
5,093
6,204
7,178
9,557
482
579
656
874
1,236
1,531
1,754
2,277
58
70
78
100
Negative Change Between Base Case and Option 1
11,528
14,976
17,544
21,878
458
595
697
869
74
96
112
140
97
126
148
184
34
45
52
65
6
7
9
11
13
19
25
34
24,084
35,702
49,445
66,956
1,136
1,384
1,601
2,132
107
129
146
195
276
341
391
508
13
16
17
22
2019
5-Year Total
Annual Average
158,577
6,298
1,015
244
472
79
242
497,168
16,562
1,523
3,862
167
519,998
20,651
3,328
798
1,549
258
742
1,463,058
50,847
4,692
12,176
541
104,000
4,130
666
160
310
52
148
292,612
10,169
938
2,435
108
129,651
5,149
830
386
64
198
406,480
13,541
1,245
3,158
137
425,146
16,884
2,721
1,266
211
607
1,196,184
41,572
3,836
9,955
442
85,029
3,377
544
253
42
121
239,237
8,314
767
1,991
88
28,926
1,149
185
244
86
14
44
90,687
3,021
278
704
30
94,852
3,767
607
798
283
47
135
266,874
9,275
856
2,221
99
18,970
753
121
160
57
9
27
53,375
1,855
171
444
20
40