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.

Societal Transformations in Models for Energy and Climate Policy: The Ambitious Next Step

“…9,[38][39][40] Three types of benefits from such collaborations can be distinguished: interdisciplinary learning, increased realism of models, and new solutions to energy and climate challenges. First, modelers and social scientists who participate in such linking exercises experience mutual learning: for example, identification of missing societal factors in models, 41,42 improved consistency between narratives and models, 43,44 or at least better awareness and appreciation of the research in other disciplines. 45 Second, these interdisciplinary teams identify specific areas where the realism of models could be improved, for instance, by representing deviations from economic rationality in electricity-sector investments 46,47 or by accounting for the public acceptance bottlenecks in models.…”

“…[77][78][79] Another recent concept is the framework of social learning for vehicle adoption, which describes the reduction of anxiety toward new technologies through social influence. 42 Other early examples of potentially quantifiable patterns that still need further investigation are the S-curves 80 or multinomial logit equations 62 for modeling technology adoption or the more general suggested laws of energy transition. 81,82 For instance, the suggested laws observe that new technologies go through exponential growth at a rate of 1 order of magnitude per decade until they reach 1% of the world's energy share and then switch to linear growth after several decades.…”

Efficiency improvement and technology choice for energy and emission reductions of the residential sector

“…Finally, while the model includes financial motivations which affect technology adoption dissagregated across income quantiles (heating/cooling demand, fuel costs, capital costs, discount rates), it does not include social dynamics of technology adoption. Previous studies that included an explicit representation of consumer archetypes (early adopters, laggards) and social influence effects showed these to influence technology diffusion (Edelenbosch, McCollum et al 2018.…”

Interaction of Consumer Heterogeneity and Technological Progress in the US Electric Vehicle Market

“…This approach was applied to solar panels in Japan [19], later dubbed "cascading diffusion", and used for residential fuel cells [20]. Another group of models focuses on the behavioral aspects of diffusion, linking to technological progress to predict technological costs over time [21][22][23].…”