The Relationship between Net Energy Use and the Urban Density of Solar Buildings
Bibliographic record
Abstract
There is a paradoxical relationship between the density of solar housing and net household energy use. The amount of solar energy available per person decreases as density increases. At the same time, transportation energy, and to some extent, household operating energy decreases. Thus, an interesting question is posed: how does net energy use vary with housing density? This study attempts to provide insight into this question by examining three housing forms: low-density detached homes, medium-density townhouses, and high-density high-rise apartments in Toronto. The three major quantities of energy that are summed for each are building operational energy use, solar energy availability, and personal transportation energy use. Solar energy availability is determined on the basis of an effective annual collector efficiency. The results show that under the base case in which solar panels are applied to conventional homes, the high-density development uses one-third less energy than the low-density one. Improving the efficiency of the homes results in a similar trend. Only when the personal vehicle fleet or solar collectors are made to be extremely efficient does the trend reverse—the low-density development results in lower net energy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".