Cool walkability planning: Providing pedestrian thermal comfort in hot climate cities
Bibliographic record
Abstract
Global warming and urbanization are increasing the number of people living in cities that experience extreme heat. This makes walking uncomfortable, unattractive, and unhealthy and causes travelers to drive for trips that could be made on foot. To address these problems hot-climate cities can create networks of shadeways (shaded sidewalks) and pedways (enclosed, climate-controlled walkways). This article introduces the Cool Walkshed Index (CWI) which rates pedestrian thermal protection from A (best) to F (worst). Currently, most urban neighborhoods have CWI E (incomplete sidewalk networks) or D (complete sidewalk networks). Moderate-heat cities should aspire to CWI C (shaded sidewalks on busy routes); high-heat cities, with temperatures that frequently exceed 38 °C (100 °F) should aspire to CWI B (most buildings located within 300 m of enclosed, climate-controlled pedways); and extreme-heat cities, with temperatures that frequently exceed 43 °C (110° F) should aspire to CWI A (most buildings are located less than 100 m of enclosed, climate-controlled pedways). Analysis in this study indicates that the additional costs of these facilities can be repaid many times over through road, parking, and vehicle savings, and increased local property values.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".