Climate Risk in Intermediate Goods Trade: Impacts on China’s Fisheries Production
Bibliographic record
Abstract
Climate change, especially extreme weather events, has significantly heightened the vulnerability of fisheries production supply chains. This study firstly investigates the input-driven climate risks through intermediate goods trade and their indirect impacts on China’s fisheries sector and constructs the Climate Risk-Trade-Production Model (CRTPM). Key findings include: (1) The input-driven climate risk indicator for China’s fisheries sector has increased over the period 1995–2020, with Brazil, Canada, the United States, Japan, South Korea, and Russia as major contributors. (2) From 1995 to 2020, rising climate risk index in Brazil and Canada negatively affected China’s fisheries output, with a 1% increase in climate risk index resulting in production declines of 0.173% and 0.367%, respectively. (3) In contrast, a reduction in the climate risk index in the United States and Japan lowered intermediate goods prices, boosting China’s output by 0.934% and 0.172%, respectively, for every 1% decrease in the climate risk index. (4) Climate risk index in South Korea and Russia, while initially increasing, eventually stabilized, having minimal impact on China’s fisheries production. It is the importance of monitoring extreme weather events to mitigate the economic vulnerabilities of China’s fisheries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".