Water footprints of nations. Volume 1: Main Report
Bibliographic record
Abstract
The water footprint concept has been developed in order to have an indicator of water use in relation to\nconsumption of people. The water footprint of a country is defined as the volume of water needed for the\nproduction of the goods and services consumed by the inhabitants of the country. Closely linked to the water\nfootprint concept is the virtual water concept. Virtual water is defined as the volume of water required to\nproduce a commodity or service. International trade of commodities implies flows of virtual water over large\ndistances. The water footprint of a nation can be assessed by taking the use of domestic water resources, subtract\nthe virtual water flow that leaves the country and add the virtual water flow that enters the country.\nThe internal water footprint of a nation is the volume of water used from domestic water resources to produce\nthe goods and services consumed by the inhabitants of the country. The external water footprint of a country is\nthe volume of water used in other countries to produce goods and services imported and consumed by the\ninhabitants of the country. The study aims to calculate the water footprint for each nation of the world for the\nperiod 1997-2001.\nThe use of domestic water resources comprises water use in the agricultural, industrial and domestic sectors. The\ntotal volume of water use in the agricultural sector is calculated based on the total volume of crop produced and\nits corresponding virtual water content. The virtual water content (m3/ton) of primary crops is calculated based\non crop water requirements and yields. The crop water requirement of each crop is calculated using the\nmethodology developed by FAO. The virtual water content of crop products is calculated based on product\nfractions (ton of crop product obtained per ton of primary crop) and value fractions (the market value of one\ncrop product divided by the aggregated market value of all crop products derived from one primary crop). The\nvirtual water content (m3/ton) of live animals is calculated based on the virtual water content of their feed and\nthe volumes of drinking and service water consumed during their lifetime. The calculation of the virtual water\ncontent of livestock products is again based on product fractions and value fractions. Virtual water flows\nbetween nations are derived from statistics on international product trade and the virtual water content per\nproduct in the exporting country.\nThe global volume of water used for crop production, including both effective rainfall and irrigation water, is\n6390 Gm3/yr. In general, crop products have lower virtual water content than livestock products. For example,\nthe global average virtual water content of maize, wheat and rice (husked) is 900, 1300 and 3000 m3/ton\nrespectively, whereas the virtual water content of chicken meat, pork and beef is 3900, 4900 and 15500 m3/ton\nrespectively. However, the virtual water content of products strongly varies from place to place, depending upon\nthe climate, technology adopted for farming and corresponding yields. The global volume of virtual water flows\nrelated to the international trade in commodities is 1625 Gm3/yr. About 80% of these virtual water flows relate\nto the trade in agricultural products, while the remainder is related to industrial product trade.\nThe global water footprint is 7450 Gm3/yr, which is 1240 m3/cap/yr. The differences between countries are\nlarge: the USA has an average water footprint of 2480 m3/cap/yr, while China has an average footprint of 700\nm3/cap/yr. The four major factors determining the water footprint of a country are: volume of consumption\na (related to the gross national income); consumption pattern (e.g. high versus low meat consumption); climate\n(growth conditions); and agricultural practice (water use efficiency).\nThe countries with a relatively high rate of evapotranspiration and a high gross national income per capita\n(which often results in large consumption of meat and industrial goods) have large water footprints, such as:\nPortugal (2260 m3/yr/cap), Italy (2330 m3/yr/cap) and Greece (2390 m3/yr/cap). Some countries with a high\ngross national income per capita can have a relatively low water footprint due to favourable climatic conditions\nfor crop production, such as the United Kingdom (1245 m3/yr/cap), the Netherlands (1220 m3/yr/cap), Denmark\n(1440 m3/yr/cap) and Australia (1390 m3/yr/cap). Some countries can exhibit a high water footprint because of\nhigh meat proportions in the diet of the people and high consumption of industrial products, such as the USA\n(2480 m3/yr/cap) and Canada (2050 m3/yr/cap).\nInternational water dependency is substantial. An estimated 16% of the global water use is not for producing\ndomestically consumed products but products for export. With increasing globalisation of trade, global water\ninterdependencies are likely to increase.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".