Advancing Municipal Natural Asset Management through Standardized Evaluation
Bibliographic record
Abstract
In Canada, many urban and near-urban ecosystems are in decline. As well, engineered infrastructure is aging, its capital and operating costs are rising, and municipal service delivery is strained. Local governments are searching for new strategies to deliver services in financially and environmentally sustainable ways. They are also looking to incorporate ecosystems and ecosystem services into their understanding of service delivery. Unfortunately, many municipalities struggle to view these ecosystems as green infrastructure that can provide local communities with a wide range of important services such as stormwater management. However, some Canadian municipalities are beginning to incorporate ecosystems and the services they provide into their asset management planning and service delivery frameworks, an approach known as municipal natural asset management. To conduct municipal natural asset management, municipalities should restore, conserve, inventory, and track ecosystems under their jurisdiction. \n \nAs more municipalities incorporate municipal natural asset management, evidence of its efficacy is required to upscale and mainstream this approach. Therefore, the purpose of this research is to evaluate municipal natural asset management programs. Evidence from this evaluation will contribute to a broadening database of the beneficial outcomes of a municipal natural asset management program. To do this, this research created a rigorous evaluation framework for municipal natural asset management and has applied it to a national cohort of five case studies. This evaluation framework includes a Program Logic Model and an Evaluation Matrix as two common evaluation tools. As well, evaluation questions, indicators, benchmarks, and a five-point, colour-coded scoring system were created for program outcomes based on four distinct outcome streams in the Program Logic Model. These four outcome streams are (i) Awareness, Capacity and Education Outcomes, (ii) Implementation Outcomes, (iii) Ecosystem Rehabilitation and Restoration Outcomes and (iv) Service Delivery Outcomes. Findings from the evaluation showed that the five municipalities received high scores for Awareness, Capacity and Education Outcome indicators and some Implementation Outcome indicators. However, the municipalities did not receive high scores in later Implementation Outcome indicators, Ecosystem Rehabilitation and Restoration Outcome indicators, and Service Delivery Outcome indicators. \n \nThese findings reveal that municipalities are aligning municipal natural asset management with existing municipal climate action initiatives. Moving forward, Canadian local governments should focus on partnerships and champions to enable municipal natural asset management, recognize municipal natural asset management as a full municipal program, and use existing tools to identify sites for ecosystem rehabilitation and restoration. Findings from the evaluation also provide insights on complex and complicated Program Logic Models, nested outcomes, and outcome streams. This evaluation framework should be improved upon so more municipalities can be evaluated simultaneously and automatically. Finally, local governments should explore using funding from COVID-19 Pandemic Recovery to integrate municipal natural asset management.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".