Indigenous‐Led Nature‐Based Solutions Align Net‐Zero Emissions and Biodiversity Targets in Canada
Bibliographic record
Abstract
Abstract Indigenous‐led Nature‐based Solutions (“Indigenous‐led NbS”), such as Indigenous Protected Conserved Areas and Indigenous Guardians programs, may represent a unique opportunity to advance climate and biodiversity targets grounded in Indigenous self‐determination. Previous studies have comprehensively explored the scope and potential environmental outcomes of Indigenous‐led NbS. Here, we build on this literature to assess how government support for Indigenous‐led NbS influences climate and biodiversity outcomes. Specifically, we estimate the contribution of Indigenous‐led NbS funded by the federal Government of Canada in conserving carbon stocks and biodiversity across terrestrial ecosystems. Using geospatial analysis and quasi‐experimental methods, our results indicate that Indigenous‐led NbS are as effective as existing Protected Areas in terms of climate change mitigation and biodiversity conservation. Moreover, our results demonstrate that government funding for Indigenous‐led NbS is associated with moderate yet significant avoided land use emissions relative to Protected Areas. Based on topic‐modeling applied to Indigenous‐led NbS descriptions, climate and biodiversity outcomes emerge from holistic approaches to governance, intergenerational knowledge exchange, and climate‐biodiversity action. Thus, government funding to Indigenous‐led NbS may align biodiversity and climate outcomes with some aspects of Indigenous self‐determination. The long‐term alignment of these outcomes will require extended and sustained funding as well as full recognition of the rights of Indigenous Peoples.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".