Virtual water trade a quantification of virtual water flows between nations in relation to international crop trade
Bibliographic record
Abstract
The water that is used in the production process of an agricultural or industrial product is called the 'virtual water' contained in the product. A water-scarce country might wish to import products that require a lot of water\nin their production (water-intensive products) and export products or services that require less water (waterextensive products). This implies net import of ‘virtual water’ (as opposed to import of real water, which is\ngenerally too expensive) and will relieve the pressure on the nation’s own water resources. Until date little is known on the actual volumes of virtual water trade flows between countries.\nThe objective of this study is to quantify the volumes of all virtual water trade flows between nations in the period 1995-1999 and to put the virtual water trade balances of nations within the context of national water\nneeds and water availability. The study has been limited to the quantification of virtual water trade flows related to international crop trade.\nThe basic approach has been to multiply international crop trade flows (ton/yr) by their associated virtual water content (m3/ton). The required crop trade data have been taken from the United Nations Statistics Division in\nNew York. The required data on virtual water content of crops originating from different countries have been estimated on the basis of various FAO databases (CropWat, ClimWat, FAOSTAT).\nThe calculations show that the global volume of crop-related virtual water trade between nations was 695 Gm3/yr in average over the period 1995-1999. For comparison: the total water use by crops in the world has\nbeen estimated at 5400 Gm3/yr (Rockström and Gordon, 2001). This means that 13% of the water used for crop production in the world is not used for domestic consumption but for export (in virtual form). This is the global\npercentage; the situation strongly varies between countries.\nConsidering the period 1995-1999, the countries with largest net virtual water export are: United States, Canada, Thailand, Argentina, and India. The countries with largest net virtual water import in the same period are: Sri\nLanka, Japan, the Netherlands, the Republic of Korea, and China. For each nation of the world a ‘water footprint’ has been calculated (a term chosen on the analogy of the ‘ecological footprint’). The water footprint, equal to the \nsum of the domestic water use and net virtual water import, is proposed here as a measure of a nation’s actual appropriation of the global water resources. It gives a more complete picture than if one looks at domestic water use only, \nas is being done until date. In addition to the water footprint, indicators are proposed for a nation’s ‘water self-sufficiency’ and a nation’s ‘water dependency’. In studying global virtual water trade flows, it is recommended to \nstart working on other products than crops as well, for instance livestock products such as meat. Another next step is to start interpreting the data and to study how governments can deliberately interfere in the current national \nvirtual water trade balances in order to achieve higher global water use efficiency.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".