Interactions between social learning and technological learning in electric vehicle futures (2024)

Edelenbosch, O. Y., McCollum, D. L., Pettifor, H., Wilson, C. (2018). Interactions between social learning and technological learning in electric vehicle futures. Environmental Research Letters, 13(12), Article 124004. https://doi.org/10.1088/1748-9326/aae948

Edelenbosch, O. Y. ; McCollum, David L. ; Pettifor, Hazel et al. / Interactions between social learning and technological learning in electric vehicle futures. In: Environmental Research Letters. 2018 ; Vol. 13, No. 12.

@article{883f0e5d18ef491ca6d312682da8c2ef,

title = "Interactions between social learning and technological learning in electric vehicle futures",

abstract = "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.",

keywords = "social influence, technological learning, transport modeling, vehicle choice",

author = "Edelenbosch, {O. Y.} and McCollum, {David L.} and Hazel Pettifor and Charlie Wilson and {Van Vuuren}, {Detlef P.}",

year = "2018",

doi = "10.1088/1748-9326/aae948",

language = "English",

volume = "13",

journal = "Environmental Research Letters",

issn = "1748-9318",

publisher = "IOP Publishing",

number = "12",

}

Edelenbosch, OY, McCollum, DL, Pettifor, H, Wilson, C 2018, 'Interactions between social learning and technological learning in electric vehicle futures', Environmental Research Letters, vol. 13, no. 12, 124004. https://doi.org/10.1088/1748-9326/aae948

Interactions between social learning and technological learning in electric vehicle futures. / Edelenbosch, O. Y.; McCollum, David L.; Pettifor, Hazel et al.
In: Environmental Research Letters, Vol. 13, No. 12, 124004, 2018.

Research output: Contribution to journal › Article › Academic › peer-review

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T1 - Interactions between social learning and technological learning in electric vehicle futures

AU - Edelenbosch, O. Y.

AU - McCollum, David L.

AU - Pettifor, Hazel

AU - Wilson, Charlie

AU - Van Vuuren, Detlef P.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - social influence

KW - technological learning

KW - transport modeling

KW - vehicle choice

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DO - 10.1088/1748-9326/aae948

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AN - SCOPUS:85060133318

SN - 1748-9318

VL - 13

JO - Environmental Research Letters

JF - Environmental Research Letters

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M1 - 124004

ER -

Edelenbosch OY, McCollum DL, Pettifor H, Wilson C, Van Vuuren DP. Interactions between social learning and technological learning in electric vehicle futures. Environmental Research Letters. 2018;13(12):124004. doi: 10.1088/1748-9326/aae948