Combining stated and revealed choice research to inform energy system simulation models: the case of hybrid electric vehicles.

This study estimated dynamic behavioural parameters for an energy-economy model (called CIMS) using discrete choice modelling techniques, focusing on hybrid-electric vehicles (HEVs) . An online survey collected stated preference (SP) data from Canadian and Californian vehicle owners under different hypothetical market conditions. Revealed preference (RP) data was collected by eliciting the year, make and model of recent vehicle purchases. SP and RP data were combined in a 'joint' multinornial logit modelling technique, yielding choice models that were more realistic and useful than models estimated from SP or RP data alone. Dynamic behavioural parameters were estimated from the joint choice models and integrated into CIMS, significantly altering HEV adoption forecasts. Policy simulations with the improved model demonstrate the potential efficiencies of policies that induce technological change by reducing a technology's non-financial costs (e.g. vehicle emissions standard), as opposed to targeting capital costs (e.g. subsidies) or fuel costs (e.g. gasoline tax).

[1]  Greg M. Allenby,et al.  Modeling Interdependent Consumer Preferences , 2003 .

[2]  Andreas Löschel,et al.  Technological Change in Economic Models of Environmental Policy: A Survey , 2002 .

[3]  Mark Jaccard,et al.  Modeling the cost of climate policy: Distinguishing between alternative cost definitions and long-run cost dynamics , 2003 .

[4]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[5]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[6]  Mark Jaccard,et al.  Combining Top-Down and Bottom-Up Approaches To Energy-Economy Modeling Using Discrete Choice Methods , 2005 .

[7]  H. Jacobsen.  Integrating the bottom-up and top-down approach to energy–economy modelling: the case of Denmark , 1998 .

[8]  Hadi Dowlatabadi,et al.  A REVIEW OF TECHNICAL CHANGE IN ASSESSMENT OF CLIMATE POLICY , 1999 .

[9]  Christoph Böhringer,et al.  The synthesis of bottom-up and top-down in energy policy modeling , 1998 .

[10]  Adam B. Jaffe,et al.  Energy-Efficiency Investments and Public Policy , 1994 .

[11]  K. Train,et al.  Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles , 1999, Controlling Automobile Air Pollution.

[12]  T Takahashi,et al.  Rising to the Kyoto challenge: is the response of Canadian industry adequate? , 2001, Journal of environmental management.

[13]  Jordan J. Louviere,et al.  A sequential approach to exploiting the combined strengths of SP and RP data: Application to freight shipper choice , 1994 .

[14]  Dale J. Poirier,et al.  Intermediate Statistics and Econometrics: A Comparative Approach , 1995 .

[15]  Mark Jaccard,et al.  Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions , 2005 .

[16]  K. Train.  Discrete Choice Methods with Simulation , 2003 .

[17]  K. Train,et al.  Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level , 1998, Review of Economics and Statistics.

[18]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[19]  Paulus Cheuk Wai Mau,et al.  Modelling consumer preferences and technological change: Survey of attitudes to hybrid vehicles , 2005 .

[20]  Jonathan Kulick,et al.  Demand-Side Management and Energy Efficiency in the United States , 2004 .

[21]  Ryuichi Kitamura,et al.  Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project , 1993 .

[22]  R. Costanza,et al.  The evolution of preferences Why 'sovereign' preferences may not lead to sustainable policies and what to do about it , 1998 .

[23]  John C. Whitehead,et al.  Environmental Risk and Averting Behavior: Predictive Validity of Jointly Estimated Revealed and Stated Behavior Data , 2005 .

[24]  Kerry-Ann Adamson,et al.  An examination of consumer demand in the secondary niche market for fuel cell vehicles in Europe , 2003 .

[25]  W. Poortinga,et al.  Household preferences for energy-saving measures: A conjoint analysis , 2003 .

[26]  David L. Greene,et al.  Future Potential of Hybrid and Diesel Powertrains in the U.S. Light-Duty Vehicle Market , 2004 .

[27]  Steve Hoeffler,et al.  Measuring Preferences for Really New Products , 2003 .

[28]  K. Train.  Discount rates in consumers' energy-related decisions: A review of the literature , 1985 .

[29]  P. Wade.  Bayesian Methods in Conservation Biology , 2000 .

[30]  J. Eyzaguirre.  Simulating Market Transformation Dynamics Using a Hybrid Energy Economy Model: A Look at the Adoption of Hydrogen Fuel Cell Vehicles , 2004 .

[31]  John M. Rose,et al.  Applied Choice Analysis: A Primer , 2005 .

[32]  John R. Hauser,et al.  Information Acceleration: Validation and Lessons from the Field , 1997 .

[33]  G. Ewing,et al.  Assessing Consumer Preferences for Clean-Fuel Vehicles: A Discrete Choice Experiment , 2000 .

[34]  Kenneth Train,et al.  Customers' Choice Among Retail Energy Suppliers: The Willingness-to-Pay for Service Attributes , 2000 .

[35]  David F. Layton,et al.  Embracing Model Uncertainty: Strategies for Response Pooling and Model Averaging , 2006 .

[36]  Stephen H. Schneider,et al.  Induced technological change and the attractiveness of CO2 abatement policies , 1999 .

[37]  David L. Greene,et al.  Feebates, rebates and gas-guzzler taxes: a study of incentives for increased fuel economy , 2005 .

[38]  K. Kurani,et al.  Analysis of Consumer Response to Automobile Regulation and Technological Change in Support of California Climate Change Rulemaking , 2004 .