Towards carbon free cities: interplay between urban density and energy demand
Bibliographic record
Abstract
Compensating area, which refers to off-site land being used if the energy demand cannot be met due to the urban arrangement of buildings, is required in a carbon-free city because the energy demand, including thermal energy (heating, cooling and hot water), power (ventilation and artificial light) in buildings and transport, need to be covered by the renewables gained on site or in the surrounding area outside of the town.This paper aims to develop a method to explore the urban density that could deliver an energy saving, land saving, and human-scaled urban situation.Various scenarios of urban densities in the cities in different climate zones were created to emphasize the comparison and the relative difference in the required compensating area.It is found that, although transportation energy consumption can be reduced by increasing number of storeys, the rate of decrease slows down as the number of storeys increases.Also, building energy consumption increases with the number of storeys because the artificial light will reach saturation (100% of hours of use) with the increased number of storey.In terms of the comparison between climate zones, the optimal scenario would be 4 to 6 storeys in cold or moderate climates.And the optimal choice would be 6 to 8 storeys in the hot and humid climates.With regard to the consideration of human scale, not only do these optimal ranges of the number of storeys provide good daylight access, but they also fall into the range of human scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".