Pro-environment consumer behaviour and electric vehicle adoptions: a comparative regional meta-analysis
Bibliographic record
Abstract
Numerous studies have explored the relationship between the environment and the adoption of electric vehicles (EVs); however, a consensus has not been reached among empirical findings. This study aims to conduct a comprehensive meta-regression analysis by synthesizing and scrutinizing data from existing literature to identify potential sources of heterogeneity among effect sizes in individual studies. To achieve this goal, we collected 429 estimates from 65 previously published studies spanning the period from 1998 to 2021. We investigate the connection between pro-environmental consumer behaviour and EV adoption at the aggregate level and compare this relationship across three distinct regions: North America, Western Europe, and emerging markets. Our research employs advanced empirical techniques, including Bayesian model averaging, frequentist model averaging, and weighted average least squares. The outcome of our analysis reveals a positive, albeit relatively weak synthesized effect of environmental factors on EV adoption. Notably, our meta-analysis findings indicate that the impact of environmental concerns on EV adoption varies significantly depending on the specific type of EV being considered. Consumers prefer versatile hybrid and plug-in hybrid electric vehicles due to fuel flexibility, especially in areas where battery electric vehicle charging infrastructure is scarce.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".