Life Cycle Assessment (LCA) of Package Deliveries: Sustainable Decision-Making for the Academic Institutions
Bibliographic record
Abstract
Due to globalization, digitalization, and competition, the number and frequency of customer requests have grown quickly over the past few years in the fast-growing commercial trade. With this steady growth, express deliveries have become one of the most important things to study and research in order to lower costs and meet more customer orders. This study uses lifecycle assessment (LCA) to analyze the environmental footprints of current delivery packaging materials, mainly comprising corrugated cardboard boxes and polystyrene foam that arrives at the University of Regina central receiving and also suggests viable alternatives to reduce the lifecycle environmental impact. The study's objective is to identify the stages that contribute the most to environmental emissions and suggest viable alternatives to reduce the lifecycle environmental impact. We sourced packaging material data from GaBi Education Database 2020 and obtained other product-specific data from published LCAs for consistency. The current study on packaging materials analysis in the base scenario revealed that the cradle-to-grave polystyrene packaging material has the highest environmental impact. These results have significant implications for decision-makers in identifying sustainable packaging materials for long-term use and for stakeholders in comprehending environmental impacts. Received: 2 March 2023 | Revised: 25 May 2023 | Accepted: 9 June 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data is available on request from the authors.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".