Blue and Green Water Footprint Assessment for China—A Multi-Region Input–Output Approach
Bibliographic record
Abstract
Research on blue and green water footprints (WF) for China has typically been carried out based on bottom-up and top-down approach using a single-region input–output table. However, this research typically lacks detail on the sectoral interrelationships which exist between China and its trading partners in other countries/regions of the world. Here, a multi-region input–output approach using the WIOD database was applied to quantify the blue and green WF for China in 2009. The quantification was conducted from both production (WFP) and consumption (WFC) perspectives. The results show that the total WFP for China in 2009 was 1152.2 km3, second only to India. At 1070.9 km3, China had the largest WFC volume in the world. The internal WF was 953.5 km3, taking the substantial share for both the WFC and WFP. Overall, China’s trade resulted in a net export of 53.5 km3 virtual water. In contrast, the agricultural sector resulted in a net import of 70.6 km3 virtual water to China, with United States, Brazil, and Canada acting as major suppliers. This study suggests that quantifying the WF of China at global level through a MRIO framework is a necessary step towards achieving sustainability for China’s water management.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".