Intellectual property policies for solar geoengineering
Bibliographic record
Abstract
Governance of solar geoengineering is important and challenging, with particular concern arising from commercial actors’ involvement. Policies relating to intellectual property, including patents and trade secrets, and to data access will shape private actors’ behavior and regulate access to data and technologies. There has been little careful consideration of the possible roles of and interrelationships among commercial actors, intellectual property, and intellectual property policy. Despite the current low level of commercial activity and intellectual property rights in this domain, we expect both to grow as research and development continue. Given the public good nature of solar geoengineering, the relationship between the public and private sectors would likely assume a procurement structure. Innovative policy approaches to intellectual property and data access that are specific to solar geoengineering are warranted. These current circumstances also present opportunities for the development of policy and norms that might soon be lost. We consider some possible approaches, and recommend a bottom‐up, primarily nonstate, voluntary “research commons” for patents and data that are related to solar geoengineering. This would facilitate information sharing and limit data fragmentation and trade secrecy. It would also provide an incentive for commons members to pledge to limit some forms of intellectual property acquisition and to assure access on reasonable terms, thereby limiting the need for enforcement. This should help reduce downstream barriers to innovation and to encourage the potential development of technologies at reasonable cost. Such a research commons might also catalyze the adoption of best practices in research and development. This article is categorized under: Policy and Governance > Private Governance of Climate Change Social Status of Climate Change Knowledge > Knowledge and Practice
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".