Sustainable and Secure Transport: Achieving Environmental Impact Reductions by Optimizing Pallet-Package Strength Interactions during Transport
Bibliographic record
Abstract
Increasing quantities of products are being transported across widely distributed supply networks; the sustainability of the packaging used to transport these goods, or unit loads, presents an area of potential concern. The most common type of unit load in the U.S. is wooden pallets supporting various configurations of stacked corrugated boxes. Research into unit load cost optimization revealed that increasing the stiffness of a pallet’s top deck can significantly affect the strength of the assembled, stacked corrugated boxes and provides opportunities to reduce the board grade required for accompanying corrugated boxes. However, there remains a knowledge gap regarding the environmental implications of this type of unit load optimization method. To address this, we conducted a life cycle analysis (LCA) to investigate the environmental implications of optimizing a unit load using this method. The environmental impacts of paired (pallet and box) unit load design scenarios (n = 108) were investigated using varied wood species, pallet top deck thicknesses, corrugated boxes sizes, corrugated flutes, and board grades. Initial and optimized unit load scenarios ensured that the unit loads offered equivalent performance. LCA results indicate that optimizing the unit load can reduce environmental impacts by up to 23%, with benefits accruing across most impact categories primarily due to the reduction in corrugated material used. Ozone depletion, the exception, was mainly affected by the increase in the amount of required pallet materials. This study provides minimum required conditions as preliminary guidance for determining the usefulness of unit load specific analysis, and a sensitivity analysis confirmed these values remain unchanged even with different transportation distances. Through the unit load optimization method, this study demonstrates that an effective way to reduce the overall environmental impact and cost of transported unit loads involves increasing the stiffness of the top decks and reducing the corrugated board grade.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".