Nations’ water footprints and virtual water trade of wood products
Bibliographic record
Abstract
Several studies addressed the water footprint (WF) of countries and virtual water (VW) trade in agricultural and industrial products, but freshwater use associated with wood products has received little attention. Yet, international trade in wood products has been growing, and forestry competes with other forest ecosystem services over limited freshwater resources. Therefore, the objective of this paper is to assess nations’ WFs of consumption of wood products, the sustainability of these WFs, and the VW flows associated with international trade in wood products. We account nations’ WFs of and VW trade in wood products with a Multi-regional Input-Output model (MRIO-forest) that tracks wood flows along global supply chains (production, processing, trade, and final uses) for the period 1997-2017 and assess the sustainability of the green and blue WF of wood products in 2017. The WF of wood production increased from 8.37 × 1011 m3/y in 1997 to 9.87 × 1011 m3/y in 2017. About 38% (3.76 × 1011 m3/y) of this WF relates to wood products for export (in 2017), which means that VW trade associated with wood products ranks in between agricultural and industrial products in absolute volumes. About 10% (9.9 × 1010 m³/y) of the green WF and 11% (3.4 × 109 m³/y) of the blue WF of wood products in 2017 are unsustainable, meaning that they are located in areas where the total green/blue WF exceeds the maximum sustainable green/blue WF. The unsustainable green WF occurs mainly in Germany, Indonesia, the Czech Republic and the UK, and mainly relates to coniferous sawnwood, paper and paperboard other than newsprint, fibreboard and non-coniferous sawnwood. The unsustainable blue WF, which is much smaller, occurs in the USA, Russia. Nigeria, Canada and India, and mainly relates to fuelwood, paper and paperboard other than newsprint, sawnwood and fibreboard. This study increases our understanding of how forest evaporation flows link to the final consumption of wood products and contributes to the wider debate on the allocation of freshwater resources in the global economy.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".