The Global Warming Potential of Geoengineering via Radiative Cooling
Bibliographic record
Abstract
Abstract This paper analyzes the potential to mitigate global warming using radiative cooling (RC) surfaces on a large scale. The study evaluates the net cooling power, radiative forcing (RF), and global warming potential of different RC materials compared to conventional construction and roofing materials, Earth's natural surfaces, and some reference cases. Key parameters for evaluating the above‐mentioned structures include their solar reflectance (albedo) and long‐wavelength infrared emissivity. Results show the cooling power that can be achieved by an ideal RC material with a solar reflectance of 100% and long‐wave infrared emissivity of 100% is 164.8 W·m −2 . In practice, materials exhibiting a cooling power as high as 160.8 W·m −2 are fabricated. Further analysis shows if 1% of Earth's surface are to be covered with this material the terrestrial RF will decrease by 1.61 W·m −2 (from 0.6 to −1.01 W·m −2 ). The results demonstrate that RC materials with high solar reflectivity and emissivity offer substantial cooling benefits and can reduce RF when implemented on large scales. The findings underscore the effectiveness of RC materials in reducing global warming and provide a valuable perspective on their role in reducing the environmental impacts of the built environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".