Bibliographic record
Abstract
We discuss the important determinants requires to develop green patents, which eventually reinforce green growth. The theoretical framework examined four elements, the enforcement of intellectual property rights (IPRs), research and development (R&D) expenditures, market size and environmental taxations. We empirically test the green patent data to test the interrelationship of green patents representing the green innovations and IPR, R&D expenditures, market size and environmental taxations. Keeping in view the availability of the data we studied 11 developed countries, which are Austria, Australia, Canada, France, Japan, Finland, Germany, Sweden, U.K and U.S. The panel data can better handled the technological change rather than the pure cross section or pure time series data. Therefore, this study used the Pooled Least Square estimation techniques like Fixed Effect Model (FEM) and random effect model (REM) for both balance period of 1995–2010 and unbalanced period from 1995–2010. We only interpreted the balance period results depicting the enforcement of IPRs has negative and significant impact on green patents while the R&D expenditures, market size and environmental taxations has positive and significant impact on the green patents e.g. development of green innovations. We believe that the enforcement of explanatory variables will eventually acquire green growth.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".