Trends in Urban Tree Canopy and Dimensions of Social Equity Across the Portland-Vancouver Metropolitan Area
Bibliographic record
Abstract
Trees are recognized as essential for maintaining a livable urban environment and the benefits they provide to people are increasingly important in the face of a changing climate. Yet, studies in Portland and elsewhere find that trees and the benefits they provide are inequitably distributed to communities differing by race/ethnicity and income. Furthermore, loss of trees to development pressure or environmental stressors presents additional challenges. To provide a regional perspective, we assessed the relationships between tree cover and communities across 27 cities and four counties in the broader Portland-Vancouver metropolitan area. By integrating new maps for tree canopy cover and canopy change from 2014 to 2020, with land use data, and Census variables describing community race/ethnicity and income, we examined relationships in current tree cover and recent changes in cover across communities as well as across different political jurisdictions and land uses. Across the region, canopy cover was 25.2% in 2020, yet varied by city (20.0-63.9%), and was lower in unincorporated areas (13.0-28.1%). A substantial disparity in tree cover was observed across communities. Canopy cover in predominantly BIPOC communities was on average 28.7% compared to 33.8% in other communities. And, canopy cover in predominantly low-income communities was 24.2% compared to 34.1%. Additionally, many of these areas saw a net canopy loss potentially compounding the disparity. This work provides a regionally consistent baseline, identifies potential priority areas for action, encourages community conversations, and informs planning efforts to achieve an equitable distribution of trees and the benefits they provide to people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".