Ten questions concerning planning and design strategies for solar neighborhoods
Bibliographic record
Abstract
Planning of neighborhoods that efficiently implement active solar systems (e.g., solar thermal technologies, photovoltaics) and passive solar strategies (e.g., daylight control, sunlight access through optimized buildings' morphology, cool pavements, greeneries) is increasingly important to achieve positive energy and carbon neutrality targets, as well as to create livable urban spaces. In that regard, solar neighborhoods represent a virtuous series of solutions for communities that prioritize the exploitation of solar energy, with limited energy management systems. The ten questions answered in this article provide a critical overview of the technical, legislative, and environmental aspects to be considered in the planning and design of solar neighborhoods. The article moves from the categorization of “Solar Neighborhood” and the analysis of the state-of-the-art passive and active solar strategies to the identification of challenges and opportunities for solar solutions’ deployment. Insights into legislative aspects and lessons learned from case studies are also provided. Ongoing trends in solar energy digitalization, competing use of urban surfaces, and multi-criteria design workflows for optimal use of solar energy are outlined, emphasizing how they generate new opportunities for urban planners, authorities, and citizens. A framework is introduced to guide the potential evolution of solar neighborhoods in the next decade and to support the design of urban areas and landscapes with architecturally integrated solar energy solutions.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".