Forest Landscape Restoration Legislation and Policy: A Canadian Perspective
Bibliographic record
Abstract
Restoring degraded ecosystems is an urgent policy priority to regain ecological integrity, advance sustainable land use management, and mitigate climate change. This study examined current legislation and policies supporting forest landscape restoration (FLR) in Canada to assess its capacity to advance restoration planning and efforts. First, a literature review was performed to assess the policy dimension of FLR globally and across Canada. Then, a Canada-wide policy scan using national databases was conducted. While published research on ecological restoration has increased exponentially in Canada and globally since the early 1990s, our results showed that the policy dimensions of FLR remain largely under documented in the scientific literature, despite their key role in implementing effective restoration measures on the ground. Our analyses have identified over 200 policy instruments and show that Canada has developed science-based FLR policies and best practices driven by five main types of land use and extraction activities: (1) mining and oil and gas activities; (2) sustainable forest management; (3) environmental impact assessment; (4) protected areas and parks; and (5) protection and conservation of species at risk. Moreover, FLR policies have been recently added to the national climate change mitigation agenda as part of the nature-based solutions and the net-zero emission strategy. Although a pioneer in restoration, we argue that Canada can take a more targeted and proactive approach in advancing its restoration agenda in order to cope with a changing climate and increased societal demands for ecosystem services and Indigenous rights. Considering the multifunctional values of the landscape, the science–policy interface is critical to transform policy aspirations into realizable and quantifiable targets in conjunction with other land-use objectives and values.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".