Analysis of Linkages between Environmental Policy Instruments and Innovation: A Case Study of End-of-Life Vehicles Technologies in Japan
Bibliographic record
Abstract
<p>Environmental policies are designed to deal with externalities either by internalizing environmental costs or imposing specific standards for environmental pollution. This study aims to examine the impact of environmental regulations related to End-of-Life Vehicles (ELV) on innovation in Japan. We determined whether there is any statistical difference in patent activity comparing the periods before and after the regulations were enacted. In order to control for exogenous factors such as business cycles, we also analyzed the ratios of ELV and total environmental patents during the same periods. Results showed that environmental regulations drive innovations and the number of ELV-related patents were larger even after controlling for such exogenous factors. We concluded that environmental policy for ELV in Japan was effective in inducing innovation. However, we also found that the weakness in these types of command and control policy is the lack of incentives for further innovation.</p>
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".