Advancing sustainable manufacturing: a case study on plastic recycling
Bibliographic record
Abstract
This paper reviews sustainable manufacturing practices by integrating environmental, economic, and social dimensions of sustainability, emphasizing that environmental aspects are most frequently addressed (53.8%), followed by economic (34.6%) and social (11.5%) dimensions. Key findings identify crucial practices for sustainability across materials, products, processes, and supply chains, particularly sustainable materials derived from natural, renewable, or waste sources. An analysis of 17,694 articles highlights trends and gaps, linking practices to life cycle stages and Sustainable Development Goals (SDGs), notably SDG#9 and SDG#12. A proposed framework emphasizes continuous environmental performance improvement through quantitative analysis using the Life Cycle Engineering (LCE) framework, enhancing competitiveness and reducing environmental impact. The LCE framework case study demonstrates how waste materials, like plastic bottles, can be repurposed as raw materials, illustrating its value, especially for small and medium-sized enterprises, and highlighting the importance of integrating sustainability from the ideation stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".