Modelling the ‘S curve’: transition dynamics in EV adoption
Bibliographic record
Abstract
Abstract Electric vehicles (EVs) offer significant potential to reduce greenhouse gas emissions from the transportation sector. This study focuses on understanding and modelling the transition from internal combustion engine vehicles (ICEs) to EVs, addressing the dynamics that drive this shift. Using a nonlinear model of opinion dynamics, we investigate the influence of effective price ratios between EVs and ICEs, EV model availability, and public charging stations on adoption rates. Historical data from Norway, a mature EV market, is utilized to validate the model and analyze the impact of policy measures, such as subsidies, on accelerating adoption. According to our model, affordability alone does not drive the transition. Factors like EV model availability and consumer trust in battery technology play crucial roles, as evidenced by the surge in hybrid vehicle adoption during the transition phase. This reflects hesitancy toward fully committing to EVs even when the technology is sufficiently mature. The model emphasizes the interplay of consumer opinion and market behaviour, highlighting the importance of policies that promote EV model availability and enhance battery reliability/increase trust in new technologies, alongside financial incentives. While the focus of this study was EV adoption, the modelling approach is relevant for the adoption of other low-carbon consumer technologies such as heat pumps. This research provides critical insights into the complexity of EV adoption and the multifaceted strategies needed to support the shift toward sustainable transportation systems. While different modelling approaches are necessary to model technology adoption, nonlinear models are particularly well-suited to capture the feedbacks and emergent dynamics that characterize EV adoption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".