Virtual Water Trade in North America - Il commercio di “acqua virtuale” nell’America del Nord
Bibliographic record
Abstract
The concept of virtual water – the amount of water used during the entire production chain of goods – represents a new tool for policymakers in water-scarce countries to shape policies in ways that improve water use efficiency. The present study extends work on the virtual water concept by testing a causality relationship between national water savings and economic growth of a water scarce country. Results of this study are twofold: (1) average annual precipitations explain the virtual water trade pattern in Mexico, and (2) there exists a Granger-causality relationship between agricultural and industrial growth and the annual volume of water saved in Mexico due to its acquisition of water-intensive crops from its North American Free Trade Agreement partners – Canada and the United States. -Il concetto di “acqua virtuale”, cioè la quantità d’acqua utilizzata durante il ciclo produttivo delle merci, rappresenta un nuovo strumento a disposizione dei governi dei paesi con scarse riserve idriche per delineare politiche atte a migliorare l’efficienza idrica. In questo studio tale concetto viene esaminato sottoponendo a test di relazione di causalità la variabile risparmio idrico (nei paesi con scarse riserve) e la variabile crescita economica. I risultati dello studio sono: (1) la media delle precipitazioni annuali spiega l’andamento del commercio di acqua virtuale; (2) esiste una relazione di Granger-causalità tra la crescita sia agricola che industriale e il risparmio annuo di acqua effettuato in Messico attraverso l’importazione dai suoi partner NAFTA, il Canada e gli Stati Uniti, di produzioni alimentari che necessitano di molta acqua.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".