The influence of social heterogeneity and behavior onconsumer choices in the transport sector

Oreane Edelenbosch of the PBL Netherlands Environmental Assessment Agency, analyzed how behavior of different consumer types, reflected in their vehicle choices, impacts adoption of advanced climate-friendly technologies.

Oreane Edelenbosch

Oreane Edelenbosch


Energy end users do not adopt energy-efficient technologies based solely on the criterion of cost-effectiveness [1] An engineer or an economist would explain this behavior as a barrier to optimality; a psychologist or sociologist as an inherent characteristic of real-world behavior and decision making. However, integrated assessment models (IAMs) often represent investment decisions and technology choices as being made by a homogeneous and “unboundedly rational” consumer group [2]. This research analyzes how behavior of different consumer types, reflected in their vehicle choices, impacts the adoption of advanced climate-friendly technologies. Or in other words: How can we represent in our models influences on vehicle choices, beyond costs and prices, in order to improve our evaluation of energy-efficiency policies?


In the IMAGE model, an IAM that simulates the interacting human and natural systems worldwide up to 2100, vehicle choice in the transport sector was represented by a multinomial logit function based on vehicle costs. In this function, the lambda determined how sensitive the model is to cost differences and represents heterogeneity:

The LDV vehicle choice model was expanded to 27 consumer groups in the United States, which vary in their living environment, attitude toward technology, and car usage. For each consumer group non-cost factors, representing behavior in decision making, based on the MA3T model [3] were added to the vehicle costs. The IMAGE model results were compared to the MESSAGE model to which a similar method was applied.


Adding non-cost factors delays the timing of alternative vehicle technology (e.g., hybrid electric vehicle [HEV], plug-in hybrid electric vehicle [PHEV], electric vehicle [EV], and full cell vehicle [FCV]) adoption and their total share in both IMAGE and MESSAGE. Refueling station availability and EV range are embedded in the non-costs, forming a hurdle to buying new technologies. In a baseline scenario, all consumer types choose to drive an internal combustion engine (ICE) vehicle. Disaggregating the consumer groups therefore does not lead to more heterogeneity in the vehicle fleet. In a mitigation scenario, where a carbon tax is applied, vehicle technologies leading to low greenhouse gas (GHG) emissions are more cost-competitive. The consumer groups make different vehicle choices, leading to a more heterogenic vehicle fleet and a longer technology diffusion time. This heterogeneity can be simulated by adjusting the lambda of the multinomial logit in the original model.


Including non-cost factors in decision making that represent disutilities for different types of consumers has a large impact on the vehicle choices made in the transport sector. Mitigating GHG emissions becomes more difficult, as consumers’ perspective on alternative technologies are an additional barrier to a transition to climate-friendly transport technologies.


[1] Mundaca L, Neij L Worrell E, McNeil M. (2010). Evaluating Energy Efficiency Policies with Energy-Economy Models. Annual Review of Environment and Resources 35(1): 305-344.

[2] Gillingham K, Newell RG, Palmer K. (2009). Energy Efficiency Economics & Policy. Washington, DC, Resources for the Future.

[3] Lin Z, Greene DL (2011). "Predicting individual on-road fuel economy using simple consumer and vehicle attributes,” SAE Technical Paper Series No. 11SDP-0014, Society of Automotive Engineers, Warrendale, PA, USA.


David McCollum and Keywan Riahi, Energy, IIASA


Oreane Edelenbosch of the PBL Netherlands Environmental Assessment Agency, is a citizen of the Netherlands. She raised private funds and 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.

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Last edited: 29 September 2015

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