Regeneration and Supply Chain Complexity: Insights From the Forest Sector
Bibliographic record
Abstract
ABSTRACT The literature on regenerative supply chains remains in its early stages and offers limited insight into the relationship between regeneration and supply chain structure. This study adopts a question‐driven exploratory approach to examine potential changes in supply chain structure for firms integrating regenerative practices at the raw material sourcing stage. Using a dataset of 838 firm‐year observations in regulated markets, this research leverages Forest Stewardship Council certification data, complemented by secondary data from other sources, to assess supplier and customer base network structures. The findings reveal distinct patterns: compared with their counterparts in non‐regenerative supply chains, manufacturers in regenerative supply chains maintain larger, more geographically dispersed supplier bases with lower interconnectedness, while origin suppliers—directly engaging in regenerative practices—develop broader customer networks spanning multiple industries. Drawing on the systems perspective, this study provides descriptive evidence that regenerative practices may necessitate the retrofitting of existing supply chains, potentially triggering network‐wide structural changes. These findings illustrate how supply chain architecture may evolve to harmonize with surrounding ecosystems.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".