Environmental Life Cycle Assessment of Commercial Analog and Digital Printing
Bibliographic record
Abstract
The commercial printing industry is in a transition from use of offset presses to inclusion of digital technology. Digital presses enable print-on-demand which moves the printing process closer to the customer, reducing distribution and storage costs and impacts to the environment. Digital presses have the ability to print shorter runs more economically than offset, have the ability to run variable print data, and use less paper from setup and calibration processes than offset presses. In this paper, an environmental life cycle assessment (LCA) for commercial printing of marketing collateral using a competitive sheet-fed offset press and an Indigo 7000 digital sheet-fed press is presented. Four damage categories are evaluated: human health, ecosystem quality, climate change, and resources. The life cycle includes materials and energy used to make the printer, paper, and consumables; energy consumption during printing, and end-of-life of paper, printer and consumables. The LCA of commercial digital printing shows that paper has the largest potential environmental impact, with press energy consumption and consumables coming next. The printer bill of materials is inconsequential by comparison. Additionally, an environmental break-even analysis demonstrates that a digital press has a lower environmental impact than an offset press when run length lies at the economic break-even point (estimated at 3,972 4-color double-sided lettersize pages).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".