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1 Understanding current and future
potential PEV buyers:
Implications for policy Jonn Axsen
Sustainable Transportation Action Research Team
(START) Simon Fraser University
Vancouver, Canada
May 11, 2016
International Energy
Agency
Transport, Energy Efficiency
and Behaviour Workshop
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Sustainable Transportation Action Research Team
(START)
Canadian PEV Study
Report now available http://www.rem.sfu.ca/people/faculty/jaxsen/cpevs/
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Following media attention for different
alternative fuels (New York Times 1980-2013)
Source: Melton, Axsen & Sperling (2016), Nature Energy
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4 4
Focusing on the Canadian market…
• Compare PEV “Pioneers” with the potential mainstream market.
• Forecast PEV sales (among potential future buyers) under different policies.
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5
1) Data collection:
The Canadian Plug-in Electric
Vehicle Study (CPEVS)
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6
Passenger Vehicle Owners
A perspective on the PEV market:
Now and future
6
New vehicle buyers
Potential
“Early Mainstream”
PEV buyers
(NVOS, 2013
n = 1754)
PEV “Pioneers”
(PEVOS, 2014/15
n = 126)
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7 Canadian “Mainstream” Survey (n = 1754),
representative of new vehicle buying households
Source: Axsen et al. (2015), Electrifying Vehicles
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8
Participants (BEV+PHEV)
across BC
8
PEV owners survey (“Pioneers”)
British Columbia, 2014-15, n = 126
0% 10% 20% 30% 40% 50%
Toyota Plug in Prius
Conversions
BMW i3
Ford Focus Electric
Fisker Karma
Ford C-Max Energi
Mitsubishi i-MiEV
Smart Fortwo
Tesla Model S
Chevrolet Volt
Nissan Leaf
Participation by Vehicle Type
26%
10%
45%
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9 CPEVS: Reflexive, multi-method design
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10 PEV interest determined through discrete choice
experiment and “design space” exercise
Source: Axsen et al. (2015), Electrifying Vehicles
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2) The PEV “Pioneers”
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12 “Images” that PEV owners associate with their PEV
0%
20%
40%
60%
80%
100% Leaf Volt Tesla
n= 59(Leaf); 32 (Volt); 12(Tesla)
Individual Pro-Societal
Source: Axsen et al. (2015), Electrifying Vehicles
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13 Preferences: PEV Pioneers love their
PEV, tend to prefer BEV (over PHEV)
Source: Axsen et al. (2015), Electrifying Vehicles
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14
Low-tech Green
- Preserving Enviro
- Prefer BEV
- Moderate UCC interest
High-tech Green
- Supporting innovation and
enviro. progress
- Prefer BEV
- Support UCC
“Other”
- Being different
- Economical (cost)
- Practicality
- Appearance
- Prefer PHEV
- Low UCC interest
Tech Enthusiast
- Supporting innovation
- Prefer BEV
- Support UCC (grid optimization)
Not tech-oriented Very tech-oriented
Not pro-environmental
Very pro-environmental
Motivations: 4 lifestyle segments of Pioneers
Source:
Axsen et al. (2015), Electrifying Vehicles
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15
3) Comparing Pioneers to
the potential “Mainstream”
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16 PEV “Pioneers” are more highly educated,
higher income, “greener” and more “techie”
30%
11% 0%
10%
20%
30%
40%
PEVOwners
Mainstream
Graduate Degree
33% 0%
20%
40%
60%
80%
100%
PEV Owners Mainstream
Household Income = +$90k
Pioneers
Mainstream
0
5
10
15
20
TechnologyOrientation (0-25)
EnvironmentalOrientation (0-25)
67%
Source: Axsen et al. (2015), Electrifying Vehicles
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17 17 Mainstream awareness is low
“How is each of the following vehicle fueled?
Source: Axsen, Bailey and Kamiya (2013), CPEVS 2013 Preliminary Report
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Mainstream buyers are more attracted to
PHEVs, not so much BEVs
Source: Axsen and Goldberg (Under Review), Transportation Research Part D
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4: PEV forecasts….
the Respondent-based Preference
and Constraint (REPAC) model
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20 Comparing PEV policies
Purchase incentives Rebates, tax breaks, fee reductions
Energy incentives Preferential electricity rates, TOU rates
Non-monetary benefits Carpool lane access, free parking
Chargers Home: incentives, building codes, streamlined permitting
Work: workplace incentives
Public: deployment, incentives
Information provision Websites, promotional material, outreach/education
Demand-focused policies
ZEV program Direct PEV deployment requirements
Efficiency standards MPG credits for PEVs
Low-carbon fuel standard Carbon reduction credits for electricity sold
R&D support Funds for various research activities
Supply-focused policies
Adapted from: Lutsey et al. (2015), ICCT White Paper
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Al-Alawi and Bradley’s (2013) recommendations for a “useful” model:
1. Better represent consumer behaviour:
– Use consumer data (survey, e.g. choice model)
– Represent financial and non-financial motivators
2. Model vehicle supply and actions of automakers
– Availability of PEV models (in dealerships)
– Variety of PEV models
– Vehicle class
3. Model national and subnational policy
– Demand-focused policies (incentives, charging access)
– Supply-focused policies (production requirements)
Source: Al-Alawi and Bradley (2013), Renewable & Sustainable Energy Reviews
Responding to critiques of alternative fuel
vehicle forecast studies
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The respondent-based preference and
constraint model (REPAC)
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
Discrete choice model
Constraints model
Latent or unconstrained demand (UD)
Stated choice experiment
Vehicle attribute
model Survey data: driving patterns, vehicle class
Tech assumptions: battery costs, fuel prices
Survey data: awareness, home charging access
Dealership access, model availability
Constrained demand (CD)
Thanks Amy Miele
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The respondent-based preference and
constraint model (REPAC)
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
CDi,j= UDi,j * HCi* PFi,j * PAi,j Unconstrained
Demand Home
charging
PEV familiarity
Constrained Demand
PEV availability
Class availability
Dealership availability Model variety
Thanks Amy Miele
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The respondent-based preference and
constraint model (REPAC)
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
CDi,j= UDi,j * HCi* PFi,j * PAi,j Unconstrained
Demand Home
charging
PEV familiarity
Constrained Demand
PEV availability
One feedback: As sales increase, consumer awareness increases
Thanks Amy Miele
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The respondent-based preference and
constraint model (REPAC)
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
CDi,j= UDi,j * HCi* PFi,j * PAi,j Unconstrained
Demand Home
charging
PEV familiarity
Constrained Demand
PEV availability
In the future, we’d like to add this feedback:
consumer preference dynamics
Thanks Amy Miele
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Adding various constraints to
understand present and short-term sales
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
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0%
5%
10%
15%
20%
25%
30%
35%
40%
2015 2020 2025 2030
Subsidy to 2020 "Weaker " demand policy
PEV
new
market
share
(BC)
Demand-focused policies can get
PEVs only so far…
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
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0%
5%
10%
15%
20%
25%
30%
35%
40%
2015 2020 2025 2030
Subsidy to 2020
Subsidy to 2030, 90% home charge access
"Stronger" demand-focused policy
"Weaker " demand policy
PEV
new
market
share
(BC)
Demand-focused policies can get
PEVs only so far…
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
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0%
5%
10%
15%
20%
25%
30%
35%
40%
2015 2020 2025 2030
Subsidy to 2020
Subsidy to 2030, 9-90% home charge access
Subsidy to 2030, 90% home charge access, “full” PEV supply
+ Strong supply policy
"Stronger" demand-focused policy
"Weaker " demand policy
PEV
new
market
share
(BC)
Supply-focused policies may be essential
for PEV “success” (e.g. with 50+ models available)
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
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PEV
new
market
share
(BC)
0%
2%
4%
6%
8%
10%
12%
14%
16%
2015 2020 2025 2030
BC baseline (no policy)
“Norway-like”
Demand policy
(Norway class share)
Comparing “Norway-like” and “California-like”
policies in Canada via REPAC
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
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PEV
new
market
share
(BC)
Comparing “Norway-like” and “California-like”
policies in Canada via REPAC
0%
2%
4%
6%
8%
10%
12%
14%
16%
2015 2020 2025 2030
BC baseline (no policy)
“Norway-like”
Demand policy
(Norway class share)
“Norway-like”
Demand policy
(BC class share)
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
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PEV
new
market
share
(BC)
0%
2%
4%
6%
8%
10%
12%
14%
16%
2015 2020 2025 2030
BC baseline (no policy)
“Norway-like”
Demand policy
(Norway class share)
“Norway-like”
Demand policy
(BC class share)
“California-like” Supply policy
Comparing “Norway-like” and “California-like”
policies in Canada via REPAC
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
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Summary PEV Pioneers
General
• Higher income, education
• Green and/or techie lifestyle
• Variety of motives
(green, techie)
PEVs
• Highly aware and engaged with
technology
• Tend to prefer BEV
• Public chargers not essential
Early Mainstream
• Lower income/education
• Variety of lifestyles
• Even wider variety of motives
• Low awareness, higher
confusion (e.g. PHEVs, UCC)
• Greatly prefer PHEVs
• Public chargers not essential
REPAC relative to most PEV forecasting literature:
1. More pessimistic no-policy scenarios (e.g. 1-2% share)
2. More pessimistic about demand-focused policies (e.g. 2-12%)
3. Suggests that supply needs to increase, perhaps through supply-focused policy
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35 California’s ZEV Mandate
Sales requirement: “the most direct policy change any state can take to ensure increased PEV deployment”
– California: ~15% PEV new market share by 2025
– Credits differ by vehicle (PHEV, EV, Fuel Cell)
– Credits can be traded among automakers (noncompliance = $5k per ZEV credit)
– US Regions: 8 other states have ZEV programs (Section 117 ZEV States)
Policy details from: Lutsey et al. (2015), ICCT White Paper
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36 Critiques of alternative fuel vehicle forecast studies
Al-Alawi and Bradley (2013) summarize several studies that forecasts market share of electric drive vehicles. Four modeling approaches:
1. Time-based diffusion models: e.g. fitting an s-curve
2. Constraints models: e.g. % of population with garage, or with a
particular commute distance
3. Discrete choice models: quantify consumer preferences, stated or
revealed preference (or data-less)
4. Agent-based models: flexible, represents decision makers
(consumers, even automakers), can be empirically-based or not
Source: Al-Alawi and Bradley (2013), Renewable & Sustainable Energy Reviews
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37 Stated preference choice experiment…
Source: Axsen et al. (2015), Energy Economics
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38 Identifying five consumer segments (or classes)
via a latent-class choice model
Source: Axsen et al. (2015), Energy Economics
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Modeling PEV policy: The respondent-based
preference and constraint model (REPAC)
Source: Wolinetz & Axsen (Under Review), Technological Forecasting & Social Change
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A ZEV mandate may be essential to
achieve 2050 GHG targets
0
2000
4000
6000
8000
10000
12000
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
2050 GHG Target
80% below 2005 GHGs
Current Policies
“Ambitious” Policies
(no ZEV)
+ZEV mandate
LCFS: 15% less GHG intensive w/ biofuels
CAFE: 60% less fuel intensive by 2050
“Ambitious”
Policies Carbon Tax: $30/t 2015 to $120/t 2050
ZEV Subsidies: $5000 in 2015 and 2020
Passenger
vehicle
GHGs
(well-to-
wheel)
Source: Sykes and Axsen (In Progress), Master’s Thesis