Current Carbon Emission Reduction Trends for Sustainability - A Review
Bibliographic record
Abstract
Carbon emissions in the supply chain have been known to contribute significantly to environmental decay. These emissions are a result of carbon dioxide and other greenhouse gases released during the burning of fossil fuels. The industry is a well-known emitter of these gases to the atmosphere. These gases end up trapping energy from the sun in the atmosphere. This has led to the governments of the world putting measures in place to minimize carbon emissions. In supply chain, during the manufacture, transportation and storage of a product a significant amount of these greenhouse gases are emitted into the atmosphere. Research about supply chain with respect to carbon emissions has been going on for decades. This is the perfect time to review the literature of what has been studied up to so far and also identify the gaps in the literature. A systematic literature review approach is employed, initially. Content analysis was used to categorize existing literature on the various topics and methods over time in the area of carbon emissions in the supply chain. Triangulation research technique is also used to analyze the current literature on carbon emissions research study in the supply chain. Thereafter, a quantitative bibliometric analysis is conducted. Based on a rigorous screening process, 138 papers were selected for analysis. This review will lead to significant opportunities for future research in related areas.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".