The long-term effect of increasing the albedo of urban areas
Bibliographic record
Abstract
Solar reflective urban surfaces (white rooftops and light-colored pavements) can increase the albedo of an urban area by about 0.1. Increasing the albedo of urban and human settlement areas can in turn decrease atmospheric temperature and could potentially offset some of the anticipated temperature increase caused by global warming. We have simulated the long-term (decadal to centennial) effect of increasing urban surface albedos using a spatially explicit global climate model of intermediate complexity. We first carried out two sets of simulations in which we increased the albedo of all land areas between ±20° and ±45° latitude respectively. The results of these simulations indicate a long-term global cooling effect of 3 × 10 −15 K for each 1 m 2 of a surface with an albedo increase of 0.01. This temperature reduction corresponds to an equivalent CO 2 emission reduction of about 7 kg, based on recent estimates of the amount of global warming per unit CO 2 emission. In a series of additional simulations, we increased the albedo of urban locations only, on the basis of two independent estimates of the spatial extent of urban areas. In these simulations, global cooling ranged from 0.01 to 0.07 K, which corresponds to a CO 2 equivalent emission reduction of 25–150 billion tonnes of CO 2 .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".