Circular economy of water: Tackling quantity, quality and footprint of water
Bibliographic record
Abstract
The evaluation of the water footprint of goods is a good step towards the evaluation of the circularity of water. The assessment of the whole life cycle of a product allows the quantification of its actual consumption of water – including direct and indirect water usage as well as water devalued through contamination. The circular economy seeks to use resources within loops that allow their conservation. The water footprint allocation can be subdivided into blue (groundwater and surface water), green (rain water) and grey (contaminated water) but it must also integrate the circularity of the water to differentiate consumption that is in closed or closable loops from that which is open-ended. For example, rainwater harvesting should be renewable as long as harvest does not materially impact runoff from catchments or recharge rates to aquifers. Surface and groundwater consumption are acceptable if the minimal environmental water requirements of associated water-dependent ecosystems are met. Environmental water requirements are unique to different settings and include maintaining a suitable availability of water of sufficient quality downstream. In a changing world, the type of ecosystems that the society wants to maintain, build or reconstruct sets the stage for defining the appropriate environmental water requirements. In that respect, zero-impact groundwater use is especially difficult to achieve – sustainability here means how much impact from the exploitation of water, society is willing to tolerate in the longer-term. Investing in water increases its value to society. The water footprint estimations must be adjusted to better integrate circular economy concepts and lessen the focus on quantification of water consumption – it is not so much the throughput that matters but where does the water come from, what happens to it after use and how circular and sustainable it is.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".