Carbon footprint assessment of a paper book : can planned integration of deinked market pulp be detrimental to climate?
Bibliographic record
Abstract
This study presents the carbon footprint of a paperback book for which the cover and inside papers were produced in the United States and printed in Canada. The choice of paper mills for both cover and page papers was based on criteria such as percentage of recycled content in the pulp mix, transport distances (pulp mill to paper mill, paper mill to print), and technologies. The cradle-to-gate assessment of greenhouse gas (GHG) emissions follows recognized guidelines for carbon footprint assessment. The results show that the production of 400,000 books, mainly distributed in North America, would generate 1,084 tonnes carbon dioxide equivalent (CO2-eq), or 2.71 kilograms (kg) CO2-eq per book. The impact of using deinked market pulp (DMP) is shown here to be detrimental, accounting for 54% of total GHG emissions and being 32% higher than reference virgin Kraft pulp. This supports findings that DMP mill GHG emissions strongly correlate with the carbon intensity of the power grid supplying the pulp mill and that virgin Kraft mills that reuse wood residue and black liquor to produce heat and electricity can achieve lower GHG emissions per tonne of pulp produced. Although contrary to common thinking, this is consistent with the Paper Task Force 2002 conclusion for office paper (the closest paper grade to writing paper or fine paper) (EDF 2002a). To get a cradle-to-grave perspective, three different end-of-life (EOL) scenarios were analyzed, all of which included a harvested wood product (HWP) carbon storage benefit for 25 years. The GHG offset concept within the context of the book editor's “carbon-neutral” paper claims is also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".