Factors Influencing the Diffusion of Battery Electric Vehicles in Urban Areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purchasing a battery electric vehicle is a type of pro-environmental behavior but the impact of such behavior on the environment becomes significant and beneficial only if a large number of individuals buy it. Therefore, getting battery electric vehicles diffused in a social system is a critical task which needs a special attention from consumers as well as governments and suppliers. This thesis aims to find out all factors influencing the rate of adoption of a battery electric vehicle by using the main constructs and important concepts of theory of diffusion of innovations proposed by Rogers (1962). The results indicate that seven factors influence the rate of adoption of a battery electric vehicle including social pressure, social prestige, usefulness for environment, difficultly of use, price, perceived risk, and knowledge and information about battery electric vehicles. Based on these factors, a road map and a set of policies to accelerate the rate of adoption of battery electric vehicles were proposed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it