Forest Products and Circular Economy Strategies: A Canadian Perspective
Bibliographic record
Abstract
The Government of Canada has embraced circular economy and is supporting an increasing number of initiatives in the field. However, implementation examples remain scattered and certain stakeholders are eager to see a greater level of commitment from policy makers. The purpose of this study is to provide a Canadian perspective on how, and to what extent, forest products are compatible with circular economy strategies. This topic was investigated through interviews with 16 Canadian experts in eco-design, circular economy, forest products and/or waste management, with a focus on construction and packaging. Efforts made by forest industries at the manufacturing stage to reduce resource consumption were acknowledged, but the implementation of other circular economy strategies, such as reuse, recycling and energy recovery, is uneven. While there is low-hanging fruit for incremental improvements, such as the processing of recovered lumber in wood panels and not mixing cardboard fibres with other paper streams to avoid downcycling, several barriers to the widespread adoption of the most promising strategies were identified. The experts consulted proposed several solutions to accelerate the deployment of circular economy strategies for forest products, for which government interventions would need to be tailored to the different policy readiness levels (PRLs) observed in the construction and packaging sectors. With circularity having economy-wide implications, setting a clear policy direction at the national level, with a circular economy roadmap for Canada for example, could accelerate coordinated implementation within and across sectors, including forest industries.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.028 | 0.019 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".