To innovate or not - that is the question A study investigating how the number of patents applications has been affected by the EU ETS
Bibliographic record
Abstract
This thesis examines how patent applications (as a proxy measure for innovation) in regulated sectors were affected by the implementation of the European Emission Trading System (the EU ETS) in 2005. The studied EU ETS-regulated sectors are cement and manufacturing of iron and steel with aviation sector as a placebo check. To test this relationship, I apply a difference-in-differences strategy with a pooled data set between 2000 and 2016. The treatment group consists of Belgium, France, Germany, Italy, Spain and Sweden. While Canada, Mexico, Russia and Taiwan represent the control group. Patent data for the EU ETS-regulated countries is defined by applications to the European Patent Office, while for non-EU ETS regulated countries it comes from their respective national patent office. Fixed effects were employed to control for the presence of clusters in sectors and country. No relationship between patent applications and the chosen EU ETS-regulated sectors due to the EU ETS can be established in this thesis. This differs from the positive effect found in previous research. Such a conclusion in this thesis holds as the overall evidence for the three EU ETS-regulated sectors.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".