Efficient and Sustainable Land Use in Logistics Through Cooperation: A Literature Review and Empirical Research
Bibliographic record
Abstract
In recent years, the issue of land consumption or land use has become increasingly important in many areas of our society. Logistics processes in particular take up a lot of space and have a significant impact on the environment. The question is how this use of land can be optimised. Based on a systematic literature review and interviews with experts in the period between May 2021 and July 2021, this paper presents indicators that constitute or influence space-efficient logistics in the context of cooperation. The results show that in addition to the established cooperation characteristics, there are other indicators that are directly related to land use. In the logistics sector, there is strong competitive pressure and, as a result, little trust between companies. It has been shown that with the help of a neutral moderator, the gap between trusting, land-efficient cooperation and one’s own entrepreneurial interests can be narrowed, and cooperation can be profitable for all participants. In addition, digitisation actually does not seem to be sufficient to meet the information needs of a cooperation. The exchange of information not only serves to automate processes, but also makes cooperation more transparent. It shows that legal and municipal requirements need to be developed. It also becomes clear that the indicators have a mutual influence on each other and cannot be considered in isolation when it comes to the actual implementation of a cooperation. By increasing the efficiency of cooperative processes and value creation, it offers the opportunity to make land use more sustainable.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".