How phase-out policies strengthen Europe's automotive industry. EMPOCI Policy briefing, Issue 1, February 2025
Bibliographic record
Abstract
Europe is committed to phasing out the sale of new petrol and diesel cars by 2035. Some assume that these net-zero sales targets are bad for business. This assumption is particularly prevalent in Germany, which relies heavily on its automotive industry. However, recent research suggests that relaxing or scrapping these phase-out policies would do more harm than good to Europe's struggling automotive industry. This is because the targets generate investment certainty and thus help companies compete in the global innovation race. This brief explains why credible phase-out policies strengthen the European automotive industry, rather than weakening it. It also covers additional key steps European policymakers could take that would bolster the industry's global competitiveness. Key Messages Phase-out policies strengthen Europe's automotive industry. They sharpen strategy, facilitate planning and reduce inertia. Changing gears would be a mistake. Phase-out policy works best when it's stable and ambitious. Complementary policies are equally crucial. Stimulating European demand for EVs is a key task now. Supporting a just transition is critical. Policies should actively address disadvantaged regions.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.018 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".