Evolution of China's water footprint and virtual water trade: A global trade assessment
Bibliographic record
Abstract
Water embodied in traded commodities is important for water sustainability management. This study provides insight into China's water footprint and virtual water trade using three specific water named Green, Blue and Grey. A multi-region input-output analysis at national and sectoral analysis levels from the years 1995 to 2009 is conducted. The evolution and position of China's virtual water trade across a global supply chain are explored through cluster analysis. The results show that China represented 11.2% of the global water footprint in 1995 and 13.6% in 2009. The green virtual water is the largest of China's exports and imports. In general, China is a net exporter of virtual water during this time period. China mainly imports virtual water from the USA, India and Brazil, and mainly exports virtual water to the USA, Japan and Germany. The agriculture sector and the food sector represent the sectors with both the largest import and export virtual water quantities. China's global virtual water trade network has been relatively stable from 1995 to 2009. China has especially close relationships with the USA, Indonesia, India, Canada, Mexico, Brazil and Australia. Trade relations, resource endowment and supply-demand relationships may play key roles in China's global virtual water footprint network rather than geographical location. Finally, policy implications are proposed for China's long term sustainable water management and for global supply chain management in general.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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".