The economic value of Canada’s National Capital Green Network
Bibliographic record
Abstract
The lack of information on the value of ecosystems contributing to human well-being in urban and peri-urban setting is known to contribute to the degradation of natural capital and ecosystem services (ES). The purpose of this study was to determine the economic value of ES in Canada's Capital Region (Ottawa-Gatineau region), so that these values can be integrated in future planning decisions. Using the valuation methods of market pricing, cost replacement, and two benefit transfer approaches (with adjustment and with meta-analysis), the value of 13 ES from five ecosystems (forests, wetlands, croplands, prairies and grasslands, and freshwater systems) was measured. The annual economic value of these 13 ES amounts to an average of 332 million dollars, and to a total economic value of over 5 billion dollars, annualized over 20 years. The largest part of this value is generated by nonmarket ES, indicating that much more emphasis should be put on the management, preservation, and understanding of processes that make up these types of ES. The work generated as part of this study is a first step towards operationalizing the concept of ES in planning. More specifically, these results can be used to raise awareness, but also as a stepping stone to improve ecosystem-wide planning in the Canada's Capital Region.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".