Data envelopment analysis on measuring greenhouse gas emissions of liner shipping companies for reducing marine pollution
Bibliographic record
Abstract
Greenhouse gas (GHG) is a main cause for climate anomalies, and thus, declining GHG emissions becomes an essential task for enterprises, especially for international liner shipping companies, to achieve the goal of corporate social responsibility (CSR). To liner shipping companies, main emitting gases are CO2, SOx, and NOx that cause marine pollution and global warming. To decline environmental warming and marine pollution, efficiency measure about GHG emissions for liner shipping companies is important. Due to data specifications of GHG, slack-based measure (SBM) data envelopment analysis (DEA) and super SBM DEA models focus on undesirable outputs are proposed and integrated into new DEA models. Through these models above, GHG emissions of liner shipping companies for varied years viewed as peer decision-making units (DMUs) are measured. Then, relative efficient DMUs are useful references for GHG emissions of liner shipping companies in emitting policies and technologies to reduce marine pollution.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".