Exploring the relationships between building and transportation stocks to meet climate change mitigation goals
Bibliographic record
Abstract
The building and transportation sectors contribute significantly to global direct and indirect carbon emissions. However, they are often treated separately when calculating carbon emissions and devising mitigation strategies. This separation overlooks the spatial and functional relationships between building and transportation stocks. Studying their complex relationships is thus crucial for developing effective climate change mitigation strategies. Among the existing examples of research on this topic, few attempts have been made to investigate relationships between building and transportation stocks across neighborhoods at the urban scale. Here, we employ spatial analysis and correlation analysis for Trondheim, Norway, to explore the relationships between building and transportation stocks at the neighborhood level within a city. Furthermore, we conduct a cross-sectional analysis of neighborhoods at increasing distances from the city center. Our results indicate that proximity to the city center is reflected in the spatial patterns of private car ownership, building density, construction year of buildings, share of detached houses, and garage floor area per capita. Neighborhoods with low per-capita levels of total vehicles, private cars, motorcycles, and vehicle road surface areas often feature high building density, old buildings, a small proportion of detached houses, and small garage floor area per capita. Notably, our study reveals that neighborhoods with shopping malls, which have high building type diversity for all buildings, do not have low private car ownership. Our results indicate that densification and polycentric development may be effective strategies to reduce indirect carbon emissions related to material carbon emissions in Trondheim. Future research could extend beyond examining correlations between building and transportation stocks by exploring their drivers and their intensity of use.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".