Sustainable Supply Chains in the Forest Bioeconomy: A Systematic Review
Bibliographic record
Abstract
The forest bioeconomy is an emerging global sector that uses forest material to make value-added bioproducts that range from pharmaceuticals to biofuels. Notwithstanding their capacity to advance various United Nations Sustainable Development Goals, forest bioproducts face considerable sustainability challenges in global supply chains associated with harvesting, processing, and transportation. Using a systematic literature review focused on challenges and solutions to sustainability in forest bioeconomy supply chains, we analyze 81 peer-reviewed studies to identify the primary sustainability challenges and their attendant solutions. We find that economic barriers to scaling the forest bioeconomy are the most commonly studied challenge, while social and environmental challenges are often marginalized. Increasing stakeholder engagement is the most commonly mentioned solution, but the limitations of stakeholder engagement are largely absent from scholarly discourse. Lastly, we identify significant gaps in the literature related to coverage of non-European countries and analysis of key sectors like mass timber construction. The results gesture to the need for more research on under-represented regions and sectors, greater attention to social and environmental supply chain challenges, and deeper engagement with adjacent literatures on the intersection of public policy with sustainable supply chain governance.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".