Cleaner production practices, implementation concerns and measurement: A systematic literature review
Bibliographic record
Abstract
Abstract This study conducts a systematic literature review to investigate the state of the art of Cleaner Production (CP) across various dimensions. First, we categorize and discuss CP practices within distinct domains, including Product Change, Change Input Materials, Technology Change, Reuse Material On‐site, and Improved Housekeeping. Subsequentially, we map and analyze the phases of CP implementation – including the planning, pre‐assessment, assessment, and implementation options – underscoring the pivotal role of senior management commitment, multidisciplinary teams, and employee training. Lastly, we delve into multiple facets of CP evaluation, covering corporate governance, measurement of CP practices, benefits of CP implementation, evaluation of CP projects, and the instruments employed. We also pinpoint contradictions and research gaps in the field and propose diverse avenues for future research. This research makes significant contributions by synthesizing, integrating and discussing existing CP categories, highlighting trends and gaps in the literature, and offering practical insights to industry practitioners, policymakers, and organizations.
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 | 0.077 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.035 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".