Sustainable Transportation Energy Pathways (STEPS)
Bunch, David S., Kalai Ramea, Sonia Yeh, Christopher Yang (2015) Incorporating Behavioral Effects from Vehicle Choice Models into Bottom-Up Energy Sector Models. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-15-13
Many different 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 investigated. 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, a 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), that model the energy system at a much higher level of detail, but simplify the relationship to the rest of the economy. Most attention has been on integrating these two types of models. However, researchers have also been concerned that E4 models, despite their vaunted high 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 the energy system.
At the same time, there is a large and well-established literature on modeling continuous models). But, the methods and approaches used in this literature differ dramatically from those used in E4 models, and so it has been unclear how to bridge the gap. This paper demonstrates a practical approach for incorporating behavioral effects from vehicle choice models into E4 models. It is based on principles of economic theory that form a common basis for all three types of models (CGE, E4, and vehicle choice/usage models). Derivations are provided that yield a theory-based approach for modifying E4 models that can be used without altering the basic software and modeling infrastructure widely used by many researchers. The approach is illustrated using an empirical application in which the behavioral assumptions from a nested multinomial choice model in an existing modeling system (MA3T) are incorporated into a TIMES/MARKAL model.