Identifying Urban Wood Construction Trends, Global Tall Timber Building Development, and the Role of Wood Promotion Policies
Bibliographic record
Abstract
This work studies the presence and evolution of wood construction in urban environments, using Santiago province in Chile as a relevant comparative case. The first part of the study analyzes the spatial and temporal distribution of wood-based structures in Santiago, showing that although wood has historically been used in low-rise housing, its presence has declined significantly due to increasing urban densification and the widespread adoption of materials like concrete for taller buildings. Currently, only 5.4% of Santiago’s buildings use wood structures, with their presence notably decreasing in the high-density municipalities of the city. Recent construction trends in Santiago show that the average building height is 12 stories, with timber buildings not exceeding 6 stories, despite the absence of specific restrictions in the building code for tall timber structures. The second part of this study contrasts these trends with the global development of tall timber buildings (six stories or more), which total approximately 300 worldwide as of 2024. The leading cities include Paris (with over 35 buildings) and London (over 17), followed by Zürich, Vancouver, and Portland. This study highlights the pivotal role of wood promotion policies in enabling this global expansion. Finally, a five-phase classification is proposed to evaluate the evolution of tall timber construction in a given city, emphasizing the role of public policy in enabling large-scale adoption, especially for cities such as Santiago.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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".