Decadal evolution of ship emissions in China from 2004 to 2013 by using an integrated AIS-based approach and projection to 2040
Bibliographic record
Abstract
Abstract. Ship emissions contribute significantly to air pollution and pose health risks to residents of coastal areas in China, but the current accounting remains incomplete and coarse due to data availability and inaccuracy in estimation method. In this study, an Automatic Identification System (AIS)-based integrated approach was developed to address this problem. This approach utilized detailed information from AIS and cargo turnover and the number of vessels calling information, thereby capable of quantifying sectoral contributions by fuel types and emissions from ports, rivers, coastal and over-the-horizon ship traffic. Based upon the established methodology, ship emissions in China from 2004 to 2013 were estimated, and those to 2040 in every five year interval under different control scenarios were projected. Results showed that for the area within 200 nautical miles (Nm) of the Chinese coast, SO2, NOx, CO, PM10, PM2.5, and hydrocarbon (HC) emissions in 2013 were 1010, 1443, 118, 107, 87 and 67 kt/yr, respectively, which doubled over these ten years. Ship source contributed ~ 10 % to the total SO2 and NOx emissions in the coastal provinces of China. Emissions from the proposed Domestic Emission Control Areas (DECAs) within 12 Nm constituted approximately 40 % of the all ship emissions along the Chinese coast, and this percentage would double when the scope is extended to 100 Nm. Ship emissions in ports accounted for about one quarter of the total emissions within 200 Nm, within which nearly 80 % of the emissions were concentrated in the top ten busiest ports of China. SO2 emissions could be reduced by 80 % in 2020 under 0.5 % global sulfur cap policy. In comparison, a similar reduction of NOx emissions would require significant technological change and would likely take several decades. This study provides solid scientific support for ship emissions control policy-making in China. It is suggested to investigate and monitor the emissions from the shipping sector in more detail in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".