A new accounting framework for assessing forest footprint of nations
Bibliographic record
Abstract
In a tele-coupled and globalized World, understanding the links between demand for wood products and land use is becoming challenging. World's economies are increasingly open and interconnected, and international trade flows of wood products are continuously growing. The increasing resource consumption of humanity is increasingly dependent on international trade. In this context, the study of forest products demand from a global-multi-regional perspective emerges as a critical issue to achieve the goal of sustainable consumption and production. In this paper, we introduce a novel accounting framework for assessing the forest footprint of nations. The method combines Multi-regional Input-Output techniques and detailed data from the Food and Agriculture Organization of the United Nations on production, consumption and bilateral trade of primary, intermediate and final wood products, advancing with respect to existing approaches with these practical distinctions for more accurate computations. The approach tracks resource flows along the global supply chain and provides detailed information on the production, transformation, international trade, and final use of 20 forest products in 223 countries, having also much wider coverage than most previous studies. We test this framework to analyse forest footprint of nations in the year 2014, showing that 22 Million hectares (Mha) of forest were harvested for the extraction of roundwood for global demand, being 9.1 Mha to satisfy the foreign demand of wood products (42% of the total forestland harvested area). Harvested forestland is concentrated in America (32%), Asia (29%) and Europe (28%), representing Africa (7%) and Oceania (4%). More than 50% of the reported forest area harvested worldwide is located in USA (15%), China (14%); Russia (11%) and Canada (8%). In terms of forest footprint, Asia shows the highest share of the total forest footprint (44%), followed by America (25%), Europe (21%), Africa (7%) and Oceania (2%). Country-wise, half is concentrated in China (24%), USA (16%), India (5%), and Russia (5%).
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".