Download TRB 2013 SR Workshop -- Axsen Presentation Slides

The “Design Space” approach:
Observing disaggregated consumer response
and preference formation
TRB Workshop: “New Perspectives in Stated-Response
Experiments: Facing up to Complexity, Subtlety and Practicality”
Presented: January 13, 2013
Dr. Jonn Axsen
Simon Fraser University
Intro
Design Space: a “novel” stated response method:
Real objective: inspire movement beyond
rational actor assumptions and SP choice methods
SP Choice Model
Design Space
Stated preference
Static preferences
Aggregated utility function
Flexible, quantitative model
Statistical analysis
Best for… Established
technology/behaviour(?)
Stated adaptation
Constructed preferences
Individual design
Rich, disaggregated data
Scenario analysis (?)
Best for…Emerging
technology/behaviour(?)
Lee-Gosselin’s (1996) typology of “Stated Response”
Behavioural
outcome
Mostly given
Mostly elicited
Constraints
Mostly given
Mostly elicited
Stated preference (SP)
Stated tolerance (ST)
Choice experiment
WTP or WTA
Stated adaptation (SA)
Stated prospect
Gaming/Simulation
“Design Space”
Open…
Source: Lee-Gosselin (1996), Conference on Household Travel Surveys: New Concepts and Research Needs
Simple Example: SP Choice Experiment
Two choices: what would you choose?
Conventional
vehicle
Electric
Vehicle
20 MPG
Electric
Range
300 miles
100 miles
Refuel (charge) time
5 minutes
8 hours
$20,000
$25,000
Fuel use
Price
I Select
Simple Example: Design Space Game
Design space: what would you design?
Conventional
vehicle
Electric
Vehicle
Fuel use
○ 20 MPG
● 25 MPG (+ $1000)
○ 30 MPG (+ $2000)
● Electric
Range
● 300 miles
○ 75 miles
● 100 miles (+ $3000)
○ 150 miles (+ $4000)
Refuel (charge) time ● 5 minutes
Price
I Select
Base: $20,000
Total: $21,000
● 8 hours
○ 3 hours (+ $1000)
Base: $23,000
Total: $26,000
The method is quite simple…
SP Choice Model
Researcher sets: …alternatives.
Respondent
selects:
Design Space
…alternatives.
…attribute levels.
…attributes available.
…resource context.
…one alternative.
…one alternative.
…attribute levels.
Who uses “design space” methods?
As far as I can tell, not many people…
• Transport: some exploration, but dominated by SP
• Planning: some similar methods
• Marketing or others?
Applications to vehicle choice:
1. EV market research (California, 1990s)
[Kurani, Turrentine, Sperling, Lee-Gosselin]
2. Plug-in hybrid survey (U.S., 2007)
[Axsen and Kurani]
3. Plug-in electric survey (San Diego, 2011)
[Axsen and Kurani]
4. BMW green electricity survey (U.S., 2012)
[Axsen and Kurani]
Theory and analysis
Many different behavioural models
(Jackson, 2005)
• Rational choice model:
– Self-interest (social?)
– Rational, deliberative, perfect info (limits?)
– Preferences are exogenous (dynamics?)
More
sophisticated
…harder
to quantify
•
•
•
•
Adjusted-expectancy value (e.g. TPB)
Moral/normative models
Habit and practice theory
Symbolism, social influence, identity
Theory
SP Choice Model
Design Space
Rational actor model
Agnostic (?)
Assumptions • Perfect information
• Consumers learn as they go
• Utility maximization
• Design reflects interest
• Preferences are generalizable • Context-specific
Preferences
…built from attributes.
…are pre-existing.
…known by consumer.
…are static across time.
…are the same across contexts.
…follow aggregate pattern.
…may relate to “whole” package.
…can be constructed.
…may be unknown.
…are formed over time.
…can change across context.
…can be very individual.
Method
•
•
•
•
•
•
•
•
Orthogonal choice set
Show series of choice sets
Respondent chooses
Estimate aggregated model
Detailed “design space” context
Carefully communicate context
Respondent designs
Observe distribution of results
(disaggregated)
What do you “do” with an SP choice model?
Observable Utilityi = B1X1i + B2X2i + B3X3i + B4X4i + ASCi
VEV = (B1 * PriceEV) + (B2 * FuelEV) + (B3 * PowerEV) + (B4 * RangeEV) + ASCEV
Significant
attributes
- Price (-)
- Range (+)
- Income (+)
Estimate
“Market share”
WTP
$85 per miles
of vehicle
range.
EV,
24.7
%
Gas,
75.3
%
Policy
Scenarios
$0.50 Fuel
tax?
What do you “do” with Design space results?
Results: individual’s designs (disaggregated)
Frequency
Distribution
50%
Compare
Consumer
Segments
E.g. by
Housing
type
% of Sample
40%
30%
20%
10%
0%
CV
HEV PHEV PHEV EV
Construct
Use model
Compare
Contexts
High price
Vs.
Low price
Build
Statistical
Model?
Types of “Design Space”: Two examples
Plug-in electric vehicle (PEV) demand project
San Diego survey, 2011 (n = 508)
Two approaches to context…
Process assumes reflexivity (iterative consumer learning)
Assess
home
recharge
availability
Complete
driving/
parking
diary
Mailed to respondent
Read
PEV
buyers’
guide
Complete
design
priority
game
Complete
purchase
design
game
Respondent survey
1) Constructing a design priority game (“points”):
What attributes should be improved first?
E.g. fuel economy vs. electricity?
Vehicle:
CV: Conventional
HEV: Hybrid
PHEV: Plug-in hybrid
Blended
All-electric capable
EV: Electric vehicle
+30% MPG
+50% MPG
10 miles
10 miles
50 miles
20
20
75
Home Recharging: Level 1
40
40
100
125
150
Level 2
Source: Axsen and Kurani (Under Review), Energy Policy
Pattern: many respondents start with fuel economy, then
move to plug-in hybrid designs; very little EV demand
50%
40% PHEV
69% PHEV
71% PHEV
45%
67% PHEV
% of Sample
40%
20
35%
miles
30%
25%
40 40
+50%
MPG
20%
15%
10%
20 20
+30%
10
150
5%
50
0%
CV HEV B
AE EV
PHEV
Round 1
CV HEV B
AE EV
CV HEV B
PHEV
Round 2
AE EV
PHEV
Round 3
CV HEV B
AE EV
PHEV
Round 4
More design options available (no cost)
Source: Axsen and Kurani (Under Review), Energy Policy
2) Constructing a purchase design game (“$$$”):
Given a price context, what would you design?
Source: Axsen and Kurani (Under Review), Energy Policy
With price context, cheaper PHEVs dominate;
still wide variety of selections
60%
66% PHEV
56% PHEV
% of Sample
50%
40%
40
40
miles
30%
20%
+50%
MPG
10%
+30%
20
10
40
20
10
0%
CV
HEV
PHEV PHEV
Blended Allelectric
40
150
50 to
150 miles
EV
Higher Price Scenario
CV
HEV
PHEV PHEV
Blended Allelectric
EV
Lower Price
Source: Axsen and Kurani (Under Review), Energy Policy
Segments: Respondent designs vary by home recharge access
80% PEV
63% PEV
77% PEV
60%
50%
% of Sample
39% PEV
40%
30%
40
20%
10%
20
0%
10
CV HEV
B
AE
EV
PHEV
No home access
(n = 90)
CV HEV
B
AE
EV
PHEV
Only Level 1
(n = 40)
CV HEV
B
AE
EV
PHEV
Can install Level 2
(n = 232)
CV HEV
B
AE
PHEV
Level 2 outlet
(n = 242)
EV
Types of results
•
•
•
•
See distributions of individuals
See patterns: fuel economy ….then electricity
See segment differences
See highest interest packages: (plug-in hybrids)
What else?
• Statistical or cluster analysis of patterns?
• Quantitative representation of preference formation?
Three novel applications of
disaggregated data…
1) Consumer-informed use scenarios
E.g. time-of-day energy use and GHG emissions
Elicited Designs
Use scenarios
+
Home recharge access
+
Driving behavior
+
Electricity model
Source: Axsen et al.(2011), Energy Policy
2) Consumer-informed technology assessment
E.g. readiness of batteries for plug-in hybrid bueyrs
Source: Axsen and Kurani (2010), Transport Policy
3) Assessing complementarity of two products
E.g. plug-in vehicles and “green” electricity
Source: Axsen and Kurani (Under Review), Environmental Research Letters
Potential applications other than…
alternative fuel vehicle purchase?
Transportation
– Fuel economy
– Vehicle-to-grid (or other “smart grid”)
– Mode choice
Household energy use
–
–
–
–
Green electricity program or home solar
Appliance purchase and use
Heating, ventilation and air conditioning (HVAC) design
Household purchase, location choice
Sustainable behaviour
– Time use, lifestyle and activity engagement
– Other “green” products (food, clothes, electronics)
– Water consumption
Summary and looking forward…
Strengths and weaknesses (according to me)
SP Choice Model
Plus
•
•
•
•
Flexible quantitative model
“Easy” statistical analysis
Well accepted in literature
Common language
Design Space
•
•
•
•
•
Rich, disaggregated data
Flexible behavioural theory
Can “see” preference formation
More “realistic” contexts
Can build individual scenarios
Minus • Unrealistic behavioural theory
• Statistical analysis can be difficult
• Assumes static preferences
• Not yet used to produce
• Can produce misleading results;
quantitative model (but could…)
language (e.g. WTP)
• Not yet generally accepted
• Lacks insight into individual
Best
for…
• Established technologies
• Familiar behaviours
•
•
•
•
New, emerging technologies
Novel behaviours and contexts
Exploring behavioural theory
Scenario construction
Priorities/challenges for stated response
1. Theory: move well beyond “rational actor”
–
–
Embrace other theories (learning, sociality, etc.)
Inform theory (inductive knowledge)
2. Assumptions: avoid misleading concepts and language
–
–
Preference formation
Valuation of the “whole” package (not attribute-based)
3. Methods: move well beyond “stated preference”
–
–
–
Stated adaptation, tolerance and priority
“Design priority” (points), “Purchase design” (dollars), priority
evaluators? What else?
Link to qualitative methods (as interview tools, generating narratives)
4. Analysis: more sophisticated (than what I’ve done)
–
–
–
–
How to quantify/analyze preference formation
Cluster design patterns/priorities
Compare attribute valuation vs. “package” valuation
New methods of valuation (beyond WTP)?
Blatant speculation on what is next…?
Pessimistic forecast (business as usual – BAU):
– Continued dominance of SP(rational actor)
– Innovation: keep refining statistical methods
– Only sporadic innovation in non-SP methods… 
Optimistic forecast (you do something…):
– Revolution: growing power of other stated response methods
– Innovation: new methods flowing from sophisticated
behavioural theories and models
– Other models/theories gain acceptance
– World learns more about people, transport, energy,
environment… 
Discussion: inspire movement beyond
rational actor assumptions and SP choice methods
SP Choice Model
Design Space
Rational Actor
Stated preference
Static preferences
Agnostic (?)
Stated adaptation
Constructed preferences
Aggregated utility function
Flexible, quantitative model
Statistical analysis
Best for… Established
technology/behaviour(?)
Individual design
Rich, disaggregated data
Scenario analysis (?)
Best for…Emerging
technology/behaviour(?)
Recent papers using “design space” games
Under Review:
• Axsen, J. and K. Kurani (Submitted). Hybrid, plug-in hybrid or electric—what kind
of electric-drive vehicles do consumers want? Submitted to Energy Policy.
• Axsen, J. and K. Kurani (Submitted). Connecting plug-in vehicles to green electricity
through consumer demand: A survey of U.S. new car buyers, Submitted to
Environmental Research Letters.
Published
• Axsen, J., K.S. Kurani, R. McCarthy and C. Yang (2011). Plug-in hybrid vehicle GHG
impacts in California: Integrating consumer-informed recharge profiles with an
electricity-dispatch model, Energy Policy, 39(3), 1617-1629.
• Axsen, J, K.S. Kurani, and A. Burke (2010). Are batteries ready for plug-in hybrids
buyers? Transport Policy, 17 (3), 173-182.
• Axsen, J, and K.S. Kurani (2010). Anticipating plug-in hybrid vehicle (PHEV) energy
impacts in California: Constructing consumer-informed recharge
profiles, Transportation Research Part D: Transport and Environment, 15(4), 212219.
• Axsen, J., and K.S. Kurani (2009). Early U.S. market for plug-in hybrid electric
vehicles: Anticipating consumer recharge potential and design priorities,
Transportation Research Record: Journal of the Transportation Research Board,
2139, 64-72.
Appendices
Constructing a design space (“purchase”)
Source: Axsen and Kurani (2009), TRR
Constructing a design space (“points”)
Source: Axsen and Kurani (2009), TRR
Game 1: PEV Design Games
Incremental prices for upgrades are based on technical literature.
•
•
All prices were framed as increments added to the “base” vehicle price (CV or HEV)
Incremental prices based on simple electric-drive price model:
–
–
•
Two price scenarios: “Higher” and “lower” battery prices
–
•
$/kWh was higher for batteries with higher power-energy ratio (W/Wh)
Incremental price includes battery, changes to engine, motor, charger, exhaust and wiring
“Higher” battery prices are double those in “lower” scenario
Base and incremental prices differ by “base” model: compact, sedan, mid-sized
SUV/truck or full-sized SUV/truck
–
Incremental prices higher for larger, heavier vehicles
Higher Price Game*
Compact
HEV
$1,080
PHEV-10 $2,710
PHEV-20 $3,160
PHEV-40 $4,070
EV-75
$5,940
EV-100
$7,570
EV-125
$9,200
EV-150
$10,820
EV-200
$14,070
Sedan
$1,290
$3,530
$4,060
$5,110
$6,920
$8,790
$10,670
$12,540
$16,290
Mid-SUV
$1,480
$4,120
$4,830
$6,240
$8,970
$11,490
$14,010
$16,530
$21,570
Full-SUV
$1,740
$5,050
$5,880
$7,540
$10,550
$13,510
$16,480
$19,450
$25,380
Lower Price Game*
Compact
HEV
$780
PHEV-10 $2,090
PHEV-20 $2,320
PHEV-40 $2,770
EV-75
$2,940
EV-100
$3,760
EV-125
$4,570
EV-150
$5,380
EV-200
$7,010
Sedan
$850
$2,600
$2,860
$3,380
$3,140
$4,080
$5,020
$5,960
$7,830
Mid-SUV
$920
$2,950
$3,300
$4,000
$4,010
$5,270
$6,530
$7,790
$10,310
Full-SUV
$1,000
$3,510
$3,920
$4,760
$4,500
$5,980
$7,460
$8,950
$11,910
*Price increases relative to the selected “base” vehicle. If respondent selects an HEV as the “base” vehicle, then
incremental prices are as shown, but less the HEV incremental price.
Game 2: Green Electricity Design Games
•
•
Each respondent’s assumed monthly household kWh demand was based on their U.S.
State of residence and housing type (detached, attached, apartment or mobile home)
Green electricity program and lease prices were based on two rates:
–
–
•
•
Higher price scenario: $0.03 per kWh covered by plan (20 to 100% of monthly kWh)
Lower price scenario: $0.015 per kWh
Residential solar only offered to respondents with solar potential (rooftop access, and
likely would have authority or permission to install)
Solar installation prices based on:
–
–
–
–
System size (180, 360, 540, 720 or 900 kWh per month)
Following economies of scale, $/watt was lower for larger systems (as detailed by IBNL, 2011)
Two price scenarios: Higher ($5.1 to $3.6/W) and lower ($3.6 to $2.5/W)—gov’t incentives included
Monthly finance rate based on 5%, 20-year rate
1. Monthly Program
Solar, wind, tidal, geothermal,
biomass, small hydro, or determined
by electric utility
Levels: 20%, 40%, 60%, 80% or 100%
of household electricity use
Price = $0.03/kWh
2. Two-Year Lease
Lease solar panels or wind
turbine (somewhere else)
3. Install Home Solar
Solar panels installed at home
Same as Monthly (#1)
Lower price scenario
Levels: 20%, 40%, 60%, 80% or 100%
of household electricity use
Price = $0.015/kWh
Same as Monthly (#1)
Savings on electric bill
None
None
180 kWh: $29/month ($5.1/W)
360 kWh: $58/month ($5.1/W)
540 kWh: $68/month ($4.0/W)
720 kWh: $86/month ($3.8/W)
900 kWh: $102/month ($3.6/W)
180 kWh: $20/month ($3.6/W)
360 kWh: $40/month ($3.6/W)
540 kWh: $48/month ($2.8/W)
720 kWh: $60/month ($2.7/W)
900 kWh: $71/month ($2.5/W)
Savings = (% solar) x Household bill
Source options
Higher price scenario