The Impact of Environmental Regulation Implementation: A Meta-Analysis
Bibliographic record
Abstract
Effective environmental regulations are crucial for biodiversity preservation and fostering innovation to mitigate environmental damage. The aim of this research is to set the groundwork for further research into the substantial role of environmental regulatory policies in promoting pro-environmental innovation. This research employs a meta-analysis approach, focusing on correlational studies published between 2001 and 2021 in English. Inclusion criteria for data selection consisted of articles addressing environmental regulations and their implementation. Correlation analyses were conducted using JASP software. The findings indicate a significant relationship between environmental regulations and their implementation, with a correlation coefficient of 0.28. With a 95% confidence interval, the estimated true score range lies between 0.14 and 0.41. Bias testing was performed using the trim and fill method, and no evidence of publication bias was found, supporting the validity of the meta-analysis results. Thus, it can be concluded that environmental regulations play a significant role in encouraging pro-environmental innovation and urging governments in each country to provide certainty about their pro-environmental policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".