Integrated Assessment Models (IAMs) have been widely used as key instruments for developing long-term energy and emission scenarios and also to identify cost-effective patterns of resource use and technology deployment over time, particularly in the context of climate change mitigation. Yet, one of the major deficiencies of most current models is their limited representation of heterogeneity on the demand side. More specifically, although systems-engineering optimization models are often very rich in representing supply-side technological details, they generally represent behavioral parameters much more simplistically. Consumer behavior cannot be ignored, however, when it comes to system-wide modeling, as it is a critical aspect of policy and decision-making. Outside of the IAM community, consumer choice has typically been modeled using non-linear simulation approaches. The objective of this project is to develop a bridging approach to bring consumer behavioral parameters – specifically for the transport sector – into a linear-programming IAM framework and to test this approach through scenario analysis.
Our particular focus has been on further enriching the end-use side of the Energy Program's prototype MESSAGE-Transport model by incorporating utility-based consumer choice decisions in the light-duty sector. The framework of the MESSAGE-Transport model consists of the vehicle price, fuel cost, and other O&M costs to make the optimal vehicle technology decisions for the system. Market penetration constraints are provided for each technology, and this partially shapes the pattern in which a new technology is adopted in the system. In the new approach we have implemented, a nested multinomial-logit consumer choice model developed by Oak Ridge National Laboratory, MA3T (Market Allocation of Advanced Automotive Technologies; Liu & Greene, 2010) is used to obtain consumer behavioral parameters, which can then be transferred to the MESSAGE-Consumer model for utilization with a linear-programming framework. As the first step, the energy service demand is disaggregated into 27 consumer groups, based on the location, risk attitude toward technology, and driving behavior profiles. On the vehicle technology side, in addition to vehicle and fuel costs, a "disutility cost" is included that is specific to the each consumer group and each vehicle technology. This cost, which comes from MA3T, captures the inconvenience costs (or non-monetary barrier costs) for the technology adoption. For each consumer group, an optimal least-cost technology decision is made, based on the vehicle cost, fuel cost, and the disutility cost parameters. These optimal results are then aggregated for the system across consumer groups. This methodology can technically be applied to any IAM that has the linear optimization framework to improve their demand-side heterogeneity, particularly in terms of behavior.
Kalai Ramea, of the University of California, Davis, is an Indian citizen residing in the United States and was funded by IIASA's United States National Member Organization. She worked in the Energy (ENE) Program during the YSSP.
Please note these Proceedings have received limited or no review from supervisors and IIASA program directors, and the views and results expressed therein do not necessarily represent IIASA, its National Member Organizations, or other organizations supporting the work.
Last edited: 19 August 2015
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