Opportunities and challenges to improve carbon and greenhouse gas budgets of the forest industry through better management of pulp and paper by-products
Bibliographic record
Abstract
Developing land use strategies to optimize carbon sinks and improve carbon footprints involves proposing efficient nature-based solutions that industries and businesses can implement while considering financial and legislative constraints. The pulp and paper industry is associated with significant greenhouse gas (GHG) emissions, primarily due to the substantial carbon dioxide (CO2) footprint of its mills. Also, some forestry operations contribute to the release of carbon to the atmosphere in the form of CO2and methane (CH4). Conversely, this industry could potentially be a significant ally in the fight against climate change by favoring forestry practices that reduce carbon emissions and increase its sequestration, namely, by adding value to industrial by-products (e.g., biosolids) instead of treating them as wastes and landfilling them. Notably, the pulp and paper industry has been seeking alternative uses of its by-products, such as fertilizers to maximize tree growth. In this paper, we identify opportunities and challenges that exist for the pulp and paper industry in regard to recycling industrial by-products to: 1) lower GHG emissions directly at the mill and 2) improve its GHG budget by increasing carbon sequestration in forests and plantations. We illustrate our analyses by describing a case study of a pulp and paper mill in southern Quebec, Canada, that uses its biosolids and other by-products as fertilizers. This case study highlights that this strategy could not only contribute to the reduction of GHGs but could also create added value and improve economic returns of forest operations.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".