Evaluating circular economy performance in the global chemical sector: a dynamic network Data Envelopment Analysis model approach
Bibliographic record
Abstract
This study evaluates the circular economy performance of global chemical companies from 2013 to 2022, focusing on production efficiency and circular efficiency. Dynamic network Data Envelopment Analysis model is employed to assess the overall, production and circular performance of firms. Additionally, management decision matrix is presented, offering firms a clearer understanding of their efficiency levels and a detailed projection analysis on the optimization adjustments needed for both resource inputs and outputs. The findings reveal varying levels of overall efficiency among the observed firms. While some companies have managed to maintain or improve their efficiency over the study period, others exhibit significant inefficiencies. Opportunities for operational streamlining and resource optimization are identified, particularly in reducing resource consumption without compromising production capacity. The analysis of circular efficiency further highlights substantial potential for improving sustainability practices, particularly through enhanced waste reuse and a reduction in environmental impact. This study provides practical implication of circular economy performance in the chemical industry, offering a new way to measure and improve resource efficiency and sustainability practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".