Recent evolution of China's virtual water trade: analysis of selected crops and considerations for policy
Bibliographic record
Abstract
Abstract. China has dramatically increased its virtual water import unconsciously for recent years. Many studies have focused on the quantity of traded virtual water but very few go into analysing geographic distribution and the properties of China's virtual water trade network. This paper provides a calculation and analysis of the crop-related virtual water trade network of China based on 27 major primary crops between 1986 and 2009. The results show that China is a net importer of virtual water from water-abundant areas of North and South America, and a net virtual water exporter to water-stressed areas of Asia, Africa, and Europe. Virtual water import is far larger than virtual water export and in both import and export a small number of trade partners control the supply chain. Grain crops are the major contributors to virtual water trade, and among grain crops soybeans, mostly imported from the US, Brazil and Argentina are the most significant. As crop yield and crop water productivity in North and South America are generally higher than those in Asia and Africa, the effect of China's crop-related virtual water trade positively contributes to optimizing crop water use efficiency at the global scale. In order to mitigate water scarcity and secure the food supply, virtual water should be actively incorporated into national water management strategies. From the national perspective, China should reduce the export and increase the import of water-intensive crops. But the sources of virtual water import need to be further diversified to reduce supply chain risks and increase resilience.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".