Quantity of plastic waste input into the ocean from China based on a material flow analysis model
Bibliographic record
Abstract
Abstract Marine plastic waste has been an important global environmental issue in recent years, and quantifying the amount of global marine plastic waste input is vital for control and mitigation. However, determining an accurate quantity of oceanic plastics is challenging because comprehensive monitoring data are difficult to obtain on national and global scales. To understand the contribution of China in global marine plastic waste input, we used a material flow analysis (MFA) method, which is included in lifecycle assessment and combines statistical data from China’s official statistics, reports, and NPO (nonprofit organization) to establish an MFA model. The model assesses the lifecycle of plastics, which starts with primary plastic, passes the stage of plastic product, and eventually becomes plastic waste. With the MFA model, the annual amount of plastic waste entering the ocean from China from 2011 to 2020 can be calculated. In 2011, 0.65 million tonnes of plastic waste entered the ocean from China, and the quantity rose slowly until 2016. A rapid decline appeared in 2018 because of China’s governmental managements and the quantity will continue to decrease until 2020. Our results indicate the amount of oceanic plastics has a strong correlation with government control measures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".