Battery Electric Vehicles can become more and more attractive to consumers through so-called social learning and technological learning, which can be mutually reinforcing if a transition is triggered.

Generally speaking, technological learning affects the timing of adoption by early adopters whereas social learning affects diffusion to other adopter groups. The two learning processes can stimulate each other in a positive feedback loop. Policy incentives stimulating EV deployment, such as a carbon tax or dedicated transport sector policies, can spark positive learning feedbacks. However, the size of this effect depends strongly on the assumed technological learning rate and social influence effect size which are key future uncertainties.

Increased electric vehicle market shares can induce technological learning which reduces technology costs while social learning stimulates diffusion from early adopters to more risk-averse adopter groups. In this way, both types of learning process interact to stimulate each other. In the absence of social learning, however, the perceived risks of electric vehicle adoption among later-adopting groups remains prohibitively high. In the absence of technological learning, electric vehicles remain relatively expensive and therefore is only an attractive choice for early adopters.

The novelty of this work is the integration of the behavioural dimensions for future technological transitions in an Integrated Assessment Model: IMAGE. This is particularly relevant here and now within our research group at COBHAM, as we presently working on how to integrate the behavioral insights of our 4 years research work into WITCH and in IAMs in general.