Ethical Framework Principles for Climate Intervention Research
Bibliographic record
Abstract
Climate intervention, also known as geoengineering or climate engineering, is the deliberate large-scale intervention in Earth's climate system to counteract global warming.1, 2 Climate intervention includes primarily carbon dioxide removal (CDR) techniques, which address the root cause of climate change by removing carbon dioxide from the atmosphere, and solar radiation modification (SRM) techniques, which offset the effects of greenhouse gas concentrations by preventing Earth from absorbing as much solar radiation.The urgency to address climate change has led to rapidly growing interest in climate intervention research.However, both CDR and SRM techniques, as well as other methods, present opportunities and risks and thus require additional governance and ethical frameworks at local, regional, and global levels.The American Geophysical Union (AGU), the world's largest association of Earth and space scientists, takes the position that a robust body of scientific evidence about climate intervention, guided by an ethical framework, should be consulted as society weighs its options for addressing climate change.Therefore, AGU has facilitated the development of this Ethical Framework for Climate Intervention Research.3 This ethical framework and its recommendations have been developed with the contributions of scientists, policymakers, ethicists, government agencies, nongovernmental organizations, and potentially impacted communities, as well as of other stakeholders on climate intervention research.These contributors and advisors sought to identify known best practices and describe them within this framework to assist researchers, institutions, governments, international and nongovernmental organizations, funders, and the private sector in their climate change and climate intervention activities.AGU contends that more knowledge about climate intervention methods and their consequences can help society make informed, just decisions about climate intervention research, including indoor and outdoor experimentation and potential deployment. 4 This framework is heavily informed by the precedent of ethical principles developed for research around nuclear weapons, 5 human cloning, 6 and genetic engineering, 7 as well as early principles considering climate intervention, such as the Oxford Principles, 8 the Asilomar Recommendations, 9 the Tollgate Principles, 10 and the Hubert code of conduct for responsible geoengineering research.11 Since its founding in 1919, AGU has set and emphasized high standards for scientific integrity and professional ethics, including the importance of "freedom to responsibly pursue science without interference or coercion" while always
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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 | 0.315 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.027 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".