The Impact of Built Environment Characteristics on Energy Consumption Using Geographically Weighted Regression in Mashhad, Iran
Bibliographic record
Abstract
Concerns over rising fuel consumption have prompted research into the influences of built environments on travel behavior. On the basis of data from origin-destination(OD) travel survey data of Mashhad (74287 trip data in 2011) and using Geographically Weighted Regression, socio-demographic characteristics, are shown to be strongly and positively associated with the fuel consumption per capita (car ownership elasticity=0.347878); we also found a positive association between distance to center and designs that are not pedestrian friendly with fuel consumption (average block size=0.147489, distance to center =0.334953) Although the study demonstrates a moderately strong negative elasticity between population density and the fuel consumption(population density = -0.259335). It suggests that the largest energy consumption reductions would come from creating compact communities which have land-use diversity and more walkable areas with pedestrian cycling infrastructure around all of the stations along transit lines.In order to enhance a sustainable urban plan, the socio-economic driving factors should be considered as one of the main element of energy consumption as well.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".