“Whose carbon is it?” Understanding municipalities role in blue carbon ecosystems management in Canada
Bibliographic record
Abstract
Blue carbon ecosystems are marine vegetated ecosystems, such as mangroves, salt marshes, kelp forests and seagrass meadows, that naturally sequester and store atmospheric carbon in their deep sediments and biomass. Their ecosystem services extend from carbon capture and storage to coastline protection and flood mitigation, making their management and protection a form of nature-based solution (NbS) for climate change adaptation. However, these ecosystems are subject to degradation and destruction from coastal development and land use change, releasing sequestered carbon into the atmosphere. In Canada, although home to the largest coastline of any country in the world, neither federal nor provincial policy explicitly address the management or protection of blue carbon ecosystems, leaving them susceptible to these threats. Coastal municipalities, however, may play an important role in the current management and protection of blue carbon. Land-use planning decisions on the local level offer an opportunity to protect and conserve coastal ecosystems and the co-benefits these ecosystems provide offer a NbS to address many climate threats faced by coastal communities. This study examines the current and potential role of municipalities in the management, protection and/or restoration of blue carbon ecosystems for sustainable land-use planning practices and carbon pollution mitigation by interviewing municipal staff from coastal municipalities across Canada. Study results demonstrated municipal interest in blue carbon ecosystems for their tangible co-benefits, not carbon sequestration and storage capacity, and that restoring blue carbon ecosystems for living shoreline projects was the most common active management strategy at the local level. However, institutional capacity, a lack of jurisdictional clarity and uncertainty surrounding blue carbon were identified as barriers that hindered municipal engagement in active management strategies, largely restricting them to play a supportive role to blue carbon projects led by local environmental non-governmental organizations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".