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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