An interdisciplinary assessment of climate engineering strategies
Bibliographic record
Abstract
Mitigating further anthropogenic changes to the global climate will require reducing greenhouse‐gas emissions (“abatement”), or else removing carbon dioxide from the atmosphere and/or diminishing solar input (“climate engineering”). Here, we develop and apply criteria to measure technical, economic, ecological, institutional, and ethical dimensions of, and public acceptance for, climate engineering strategies; provide a relative rating for each dimension; and offer a new interdisciplinary framework for comparing abatement and climate engineering options. While abatement remains the most desirable policy, certain climate engineering strategies, including forest and soil management for carbon sequestration, merit broad‐scale application. Other proposed strategies, such as biochar production and geological carbon capture and storage, are rated somewhat lower, but deserve further research and development. Iron fertilization of the oceans and solar radiation management, although cost‐effective, received the lowest ratings on most criteria. We conclude that although abatement should remain the central climate‐change response, some low‐risk, cost‐effective climate engineering approaches should be applied as complements. The framework presented here aims to guide and prioritize further research and analysis, leading to improvements in climate engineering strategies.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".