Evaluating the Progress of Municipal Natural Asset Management through Monitoring & Evaluation
Bibliographic record
Abstract
Contemporary environmental and land-use planning in many Canadian municipalities is challenged with two key problems that have grown with increasing urban development: 1) natural ecosystems decline and 2) grey infrastructure service failure. The 2020 Living Planet Report Canada highlighted the continuing decline of many natural ecosystems in remote areas and near to sprawling Canadian cities. These risks are heightened, with the possibility of climate change-driven extreme weather resulting in grey infrastructure failure. A possible approach for slowing or reversing these concerns is the use of ecosystem services provided in the form nature-based solutions (NBS). An emerging NBS practice is the implementation of Natural Asset Management (NAM). NAM is being applied by municipalities, insufficient monitoring is causing a lack of evidence that would be required to demonstrate NAM’s ability to counter ecosystem decline and grey infrastructure service loss. \nWe applied a standardized evaluation framework to assess NAM progress in six case-study municipalities in Canada. Data were collected with extensive review of municipal documents (e.g., Official Plan changes, Council meeting notes, budget allocations) and interviews with key municipal decision-makers. Data analysis was performed through thematic coding. The analysis reveals that while many municipalities are increasing their awareness and capacity for NAM implementation, large barriers to progress remain including limited enabling policies and lack of effective municipal governance. Especially changes in government and administrative organization tend to undermine the required long-term efforts in support of NAM. Municipalities must overcome these barriers to allow further progress towards restoration and conservation of natural assets that would improve the state of urban natural ecosystems and urban service delivery. An increasingly engaged public might provide pressure on municipalities to enhance permanence of NAM efforts and require accountability for NAM progress.
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 | 0.081 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".