Intellectual Property Rights and Competition Law for Transfer of Environmentally Sound Technologies
Bibliographic record
Abstract
Battling against climate change, “a common concern of humankind,” is the most prominent global challenge of this century, and Environmentally Sound Technologies (“ESTs”) are the main tools to fight this battle. This article examines the juxtaposed role of Intellectual Property Rights (“IPRs”) and competition laws in facilitating wide-scale innovation and transfer of ESTs in developing and least developed countries. This article covers diverse IPRs, including patents and trade secrets. The discussion and analysis of the IPRs are based on the Agreement on Trade-Related Aspects of Intellectual Property Rights (“TRIPS”). And the discussion on competition law mainly focuses on competition related regulation of the European Union (EU). The article concludes with an examination of whether the current state of relevant national IPRs and competition laws can facilitate the transfer of ESTs in Bangladesh, which is considered to be one of the most climate change affected countries in the world. The article finds that the adoption of a suitable IPR regime can facilitate innovation and transfer of ESTs to developing and least developed countries. However, some countries can facilitate innovation and transfer of ESTs by using TRIPS’ flexibilities like compulsory licensing. Competition laws can also facilitate innovation and transfer of ESTs through expanding EST markets by preventing abuse of IPRs, for which countries’ competition laws should include guidelines. Finally, the article finds that the current relevant IPRs and competition related laws of Bangladesh are not suitable enough for creating a favorable environment for innovation and transfer of ESTs. Hence, this paper recommends amending these domestic laws in light of TRIPS and on the basis of national interests of Bangladesh.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".