The influence of ecosystem service values on green infrastructure and urban development: insights from 5 Canadian municipalities
Bibliographic record
Abstract
To restore critical ecosystem services (ES) often lost during urbanization, municipalities have begun to implement green infrastructure (GI). Much research has addressed the incorporation of ES science into policy. However, there is a paucity of research focused on how ES values, defined as the largely non-monetary preferences of certain ES over others, of private and public stakeholders influence the uptake, design, and mainstreaming of GI through urban development processes. This study uses key informant interviews (n = 28) and document analysis of municipal plans from five Canadian municipalities to understand the role ES values play in shaping urban land development, specifically as it relates to implementation of GI. This study finds that ES values found in planning policy are largely implicit; stakeholder- and context-specific; and, influence how different GI are implemented in decision-making. The two main drivers behind ES values are local ES demand and stakeholder profile. These then influence the type and design of GI. Grounded in urban planning and land use, this paper contributes to literature at the intersection of ES values and urban GI provision; and, aim’s to support policymakers and practitioners working in this space.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".