World scenario of green patents: Perspectives and strategies for the development of eco-innovations
Bibliographic record
Abstract
The purpose of the present research was to analyze the global scenario for green patents connected with waste management areas, alternative energies, agriculture, transportation, energy conservation and the prospecting about hybrid cars. The patents analyzed were filed from 1979 to 2011. The data collection method consisted of a technological forecast about the Green Technologies. The research was carried out on the patent base Derwent Innovations Index from Web of Science. Only, 123 Green Technology patents were found in nine countries, including the United States, China, Russia, Germany, Spain, Australia, Canada, Britain and Taiwan. Indeed, 727 technological patents related to hybrid cars in sixteen countries including the United States, Japan, Germany, Spain, France, Russia, India, South Korea, Britain, Canada, Austria, Belgium, Holland and Hungary were found. The United States is leader in the ranking of Green Technologies and in hybrid car patents. However, countries such as Japan, China and Germany demonstrated a considerable increase. This study contributes toward other studies that focus on the acceleration of decisions in applications for inventive patents and aims to identify new technologies which can be quickly used by the productive sector and universities stimulating the licensing and encouraging the innovation in many countries. Key words: Green patents, eco-innovations, intellectual property, hybrid cars.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".