Analysis and Modeling of Value Creation Opportunities and Governing Factors for Electric Vehicle Proliferation
Bibliographic record
Abstract
This research presents a comprehensive analysis of electric vehicle (EV) proliferation factors and various monetary and non-monetary value streams emerging in the EV domain. A comprehensive mathematical model is implemented to study EV proliferation and the resulting market share applicable to any geography and jurisdictional regime. Further, a novel framework is presented to analyze the interdependency between EV proliferation factors and value streams. The proposed model and framework can be leveraged to quantifiably evaluate the timeline available for grid operators to accommodate EV growth while utilizing those as Distributed Energy Resources (DERs) to improve grid reliability, commercial value, and environmental benefits. Compared to the previous studies, the analysis indicated that if all the factors which impact EV proliferation are addressed simultaneously, EV market share can surpass the internal combustion engine vehicle (ICV) in as quickly as 15–20 years. The study also highlighted the importance of policy making around EVs, which can offset EV market share by up to 10% between two countries following similar sustainability goals. Therefore, the study also helps aid decision making around policies and technology investments by public and private sector organizations in the space of EV.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".