Virtual Water and Water Footprint: Bibliometric Valuation of its Scientific Analysis
Bibliographic record
Abstract
The concepts of virtual water and water footprint are complementary tools for understanding the direct and indirect use of water in anthropogenic processes. While the former refers to the amount of water required to produce a product (along with the direct impacts this generates on other users, in different regions, or on water quality), the latter addresses the indirect use of this resource in detail. In this context, the present study conducts a comprehensive bibliometric analysis of scientific publications on these concepts between 2001 and 2023, using information available in the Scopus database, supported by VOSviewer for mapping and STATA for generating graphs. The results highlight the prevalence of transdisciplinary approaches led by contributions from the People's Republic of China, the United States, England, the Netherlands, Italy, Germany, and Canada. Grounded in environmental, biological, and socioeconomic sciences, these approaches have provided precise insights into the diverse direct and indirect anthropogenic uses of water resources. In particular, China stands out as a leader in knowledge generation and in providing institutional support for research on virtual water and Water Footprint. Beyond these results, it is concerning that research activity on these topics is primarily conducted by developed countries, while the contribution from developing countries continues to lag behind. This underscores the importance of promoting equitable participation in research to address global water challenges.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.020 | 0.043 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".