Does digitalisation affect the adoption of electric vehicles? New regional-level evidence from Google Trends data
Bibliographic record
Abstract
Digitalisation is an important dimension that contributes to fostering the adoption of electric mobility. We investigate this unexplored topic by focusing on the regional level of analysis, and presenting new data and evidence for a large number of regions in Europe, Canada and the United States. The empirical analysis makes use of Google Trends data. It constructs new indicators of digitalisation and the adoption of electric vehicles, as measured by Google search queries. The new dataset contains indicators for 182 regions in 15 countries for the period 2010–23. We use this dataset to carry out a time-series analysis (vector error correction (VEC) model) of the relations between digitalisation and electric vehicles in each region. The results show that digitalisation is an important factor that has fostered the adoption of electric vehicles in the last decade. The analysis, though, also points out that there is considerable heterogeneity in the time-series results among regions in our sample. Digitalisation has a more visible effect on electric mobility for regions that have higher gross domestic product per capita, better internet infrastructures, a young and well-educated workforce, and higher population density.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".