Interactions between social learning and technological learning in electric vehicle futures

Edelenbosch, Oreane, McCollum, David, Pettifor, Hazel, Wilson, Charlie ORCID: https://orcid.org/0000-0001-8164-3566 and Van Vuuren, Detlef (2018) Interactions between social learning and technological learning in electric vehicle futures. Environmental Research Letters, 13 (12). ISSN 1748-9326

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Abstract

The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling transition pathways helps identify critical drivers and uncertainties. Global integrated assessment models (IAMs) have been used extensively to analyse climate mitigation policy. IAMs emphasise technological change processes but are largely silent on important social and behavioural dimensions to technological transitions. Here, we develop a novel conceptual framing and empirical evidence base on social learning processes relevant for vehicle adoption. We then implement this formulation of social learning in IMAGE, a widely-used global IAM. We apply this new modelling approach to analyse how technological learning and social learning interact to influence electric vehicle transition dynamics. We find that technological learning and social learning processes can be mutually reinforcing. 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 only for early adopters an attractive choice. This first-of-its-kind model formulation of both social and technological learning is a significant contribution to improving the behavioural realism of global IAMs. Applying this new modelling approach emphasises the importance of market heterogeneity, real-world consumer decision-making, and social dynamics as well as technology parameters, to understand climate mitigation potentials.

Item Type:	Article
Uncontrolled Keywords:	sdg 13 - climate action ,/dk/atira/pure/sustainabledevelopmentgoals/climate_action
UEA Research Groups:	University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Groups > Environmental Social Sciences
Related URLs:	http://iopscience.iop.org/article/10.108...
Depositing User:	LivePure Connector
Date Deposited:	31 Oct 2018 15:30
Last Modified:	18 Aug 2023 00:21
URI:	https://ueaeprints.uea.ac.uk/id/eprint/68723
DOI:	10.1088/1748-9326/aae948

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