Bibliographic record
Abstract
Climate engineering, also known as geoengineering, refers to the deliberate large-scale intervention in Earth’s climate system with the aim of mitigating the adverse effects of climate change. As global temperatures rise and conventional mitigation strategies lag behind, interest in these technologies is growing. Climate engineering is broadly classified into two categories: Carbon Dioxide Removal (CDR) and Solar Radiation Management (SRM), each with distinct methodologies, objectives, and risk profiles. While proponents argue that these approaches may offer a critical backup plan in case of climate emergency, critics warn against their uncertain long-term impacts and ethical implications. This paper explores both the environmental and ethical dimensions of climate engineering. It analyzes key techniques under CDR and SRM, potential environmental disruptions, governance gaps, and moral challenges, including issues of consent, intergenerational justice, and equity. Real-world case studies such as the SPICE project in the UK, ocean fertilization by the Haida Nation in Canada, and Iceland’s CarbFix initiative are examined to highlight the practical, legal, and societal dilemmas posed by geoengineering. Through an interdisciplinary lens, this study argues that any advancement in climate engineering must be guided by transparent, inclusive governance frameworks that prioritize precaution, sustainability, and justice.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| 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".