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Enregistrement W6997309092

Water footprints of nations. Volume 1: Main Report

2004· report· en· W6997309092 sur OpenAlexaboutno aff

Notice bibliographique

RevueData Archiving and Networked Services (DANS) · 2004
Typereport
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésVirtual waterWater useWater resourcesVolume (thermodynamics)Water flowFootprintFarm waterAgriculture
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,061
Score d'incertitude au seuil0,121

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0090,023
Études des sciences et des technologies0,0000,000
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0180,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,293
Écart entre enseignants0,257 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2004
Routes d'admission1
Résumé présentoui

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