May 8, 2015


Incorporating Behavioral Effects from Vehicle Choice Models into Bottom-Up Energy Sector Models


Friday, May 8, 2015, 1:40 p.m. – 3:00 p.m.


1605 Tilia, Room 1103, West Village


Many types of models are used for evaluating climate-change-related programs and policies, because analysis requirements can vary widely depending on the specific nature of the problem being addressed.  Limitations on data and methodology typically ensure that models have various strengths and weaknesses, requiring researchers to make tradeoffs when choosing models.  In the case of energy systems, one frequent distinction is between “top down” models (e.g., computable general equilibrium, or CGE models) that address energy systems within the context of the larger economy, versus “bottom up” models (e.g., so-called E4, or “energy/economy/environment/engineering” models), which model the energy system at a much higher level of detail, but simplify the relationship to the rest of the economy.  Recently researchers have been concerned that E4 models, despite their higher level of detail, produce results that are an unrealistic representation of consumer market behavior, calling into question their value for making policy decisions.  This is particularly true for household vehicle technology choice, an important sub-sector of many E4 models.

At the same time, there is a large and well-established literature on behavioral modeling of household vehicle choice and usage decisions that relies on discrete and discrete-continuous choice models.  This seminar will discuss how behavioral effects from these models can be incorporated into E4 models in a theoretically consistent way to produce more realistic results.  A key point is that all the model types (CGE, E4, and vehicle choice/usage models) can be derived from the same standard model of the economy, differing only in the simplifying assumptions used to develop alternative methods.  Understanding these theoretical underpinnings leads to an approach for modifying E4 models currently used by many researchers, without requiring any changes to existing modeling software.  We demonstrate the approach with an application using TIMES/MARKAL.

This talk places a heavy emphasis on reviewing aforementioned aspects of economic theory that form the basis for a variety of models used by ITS researchers, and could be of interest to current students who are relying on these models.

Various aspects of this work were done in collaboration with Kalai Ramea, Sonia Yeh, Chris Yang, and Joan Ogden.

Biographical Sketch

David Bunch received his PhD in Mathematical Sciences from Rice University (1985), and has been on the faculty of the UC Davis Graduate School of Management since that time.  He has been affiliated with ITS Davis since its inception, and has worked extensively on development of new methodologies and approaches for modeling consumer choice, particularly in the area of vehicle markets.  He has received support from both state and federal sponsors (e.g., CEC, ARB, DOT, DOE) to develop models for evaluating policies such as ZEV, Pavley, and CAFE, and economic incentive programs such as feebates.

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