Toward an evidence‐informed, responsible, and inclusive debate on solar geoengineering: A response to the proposed non‐use agreement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A prominent recent perspective article in this journal and accompanying open letter propose a broad international “non‐use agreement” (NUA) on activities related to solar geoengineering (SG). The NUA calls on governments to renounce large‐scale use of SG, and also to refuse to fund SG research, ban outdoor experiments, decline to grant IP rights, and reject discussions of SG in international organizations. We argue that such pre‐emptive rejection of public research and consultation would deprive future policy‐makers of knowledge and capability that would support informed decisions to safely and equitably limit climate risk, sustain human welfare, and protect threatened ecosystems. In contrast to the broad prohibitions of the NUA, we propose an alternative near‐term pathway with five elements: assess SG risks and benefits in the context of related climate risks and responses; distinguish the risks and governance needs of SG research and deployment; pursue research that treats uncertainties and divergent results even‐handedly; harness normalization of SG as a path to effective assessment and governance; and build a more globally inclusive conversation on SG and its governance. These principles would support a more informed, responsible, and inclusive approach to limiting climate risks, including judgments on the potential role or rejection of SG, than the prohibitory approach of the NUA. This article is categorized under: Climate and Development > Social Justice and the Politics of Development Policy and Governance > Multilevel and Transnational Climate Change Governance Policy and Governance > National Climate Change Policy
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it