Legal advice on the Chinese compensatory fund system for oil pollution damage caused by ships from the perspective of marine environmental governance
Bibliographic record
Abstract
As one of the primary obstructive factors for marine environmental governance, the frequent occurrence of oil pollution damage caused by ships has resulted in the establishment of compensation funds, such as the Oil Spill Liability Trust Fund of the United States, Ship Oil Pollution Fund of Canada and International Oil Pollution Compensation Fund (IOPC). Frequently suffering from marine oil pollution, China has extended considerable effort in marine environmental governance. Following the introduction of the ‘green principle’ into the Civil Code, China attached increasing significance to the legislation including compensation for oil pollution damage caused by ships. China formally established a compensation fund in 2012, and the past decade has witnessed the burgeoning development of the Chinese Ship-source Oil Pollution Compensation Fund (CSOPC), in addition to several defects which impede the fund from achieving the goal of marine environmental governance. As a national fund that is independent of the IOPC, the CSOPC adopts several regulations that are distinctive from internationally recognized practice; for instance, not recognizing pure economic loss within the scope of compensation. Such unique parameters, though partially originating from the national conditions in China, have resulted in glaring defects, including incomplete compensation scope and inappropriate compensation measures. Given the above problems, this study endeavours to provide several legal recommendations from the perspective of macro policies for improving the top-level design of the system, enhancing oil pollution compensation capabilities, and promoting the internationalization process. The study proposes two potential regulatory paths for innovation; namely, enlarging the range of compensation and establishing an essential emergency fund. From the perspective of protecting the rights of the victims of oil pollution damage and safeguarding the public interests of the ocean, this study puts forward relevant legal suggestions, which are expected to make valuable contributions to improving the compensation system for oil pollution damage caused by ships in China and promoting the governance of the marine environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".