Interactions between social learning and technological learning in electric vehicle futures

15713 10 86371 2 244992 document 86371 Edelenbosch_2018_Environ._Res._Lett._13_124004.pdf application/pdf f93b98df6475f3af236040ae7a3b7f0e MD5 1231449 2019-01-28 14:36:27 https://pure.iiasa.ac.at/id/eprint/15713/1/Edelenbosch_2018_Environ._Res._Lett._13_124004.pdf 15713 1 1 application/pdf text en public cc_by_4

Edelenbosch_2018_Environ._Res._Lett._13_124004.pdf

published 86372 2 245007 document 86372 lightbox.jpg image/png 9104adc1997173cd0e77be8097f6f151 MD5 8710 2019-01-28 14:46:35 https://pure.iiasa.ac.at/id/eprint/15713/2/lightbox.jpg 15713 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/86371 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/86371 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/86371 archive 353 disk0/00/01/57/13 2019-01-28 14:43:33 2021-08-27 17:31:20 2019-01-28 14:43:33 article show Edelenbosch O. McCollum D. 8484 Pettifor H. Wilson C. 8428 0000-0001-8164-3566 van Vuuren D. Interactions between social learning and technological learning in electric vehicle futures pub prog_ene prog_tnt prog_ysp social influence; technological learning; transport modeling; vehicle choice The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling future 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 future 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 is only an attractive choice for early adopters. 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. 2018-12 Institute of Physics Publishing (IOP) 10.1088/1748-9326/aae948 203 333 public Environmental Research Letters 13 12 e124004 TRUE 1748-9326 Advanced Model Development and Validation for Improved Analysis of Costs and Impacts of Mitigation Policies (ADVANCE, FP7 308329) Advanced Model Development and Validation for Improved Analysis of Costs and Impacts of Mitigation Policies (COBHAM, FP7 336155) FALSE yes info:eu-repo/grantAgreement/EC/FP7/308329/EU//ADVANCE; info:eu-repo/grantAgreement/EC/FP7/336155/EU//COBHAM info:eu-repo/semantics/article info:eu-repo/semantics/openAccess