The Law and Economics of Eco-Labels
Bibliographic record
Abstract
By late 2012, the Ecolabel Index had 432 eco-labels on record. In the first quarter of 2010, \nthe same Index measured only 340. This reflects an increase in the interest in eco-labelling \nin the last couple of years. While it is unclear why this sudden interest in eco-labelling, it is \ntrue that markets for certified goods have become more visible and relevant in some sectors. \nFor instance it has been reported that up to 20 percent of world exports of bananas are \ncertified. Considering that in 2008 the total value of international banana trade was \nestimated at US$ 5.8 Billion per year, 20 percent is quite significant. Certified coffee \nrepresents 17 percent of global production. In the US alone the estimated value of the coffee \nmarket is of US$ 19 billion per year, which again makes certified coffee quite important. \nHowever, in other sectors such as forestry, fisheries, cocoa, cotton, and tea certification is \nrelatively small. In the forestry sector, the Forest Stewardship Council (FSC) has certified the \nequivalent of 5% of the worlds productive forests, which is relatively small, but it is the \nequivalent of 125 million hectares of forest over 80 countries. Moreover the value of FSC \nlabelled sales is estimated at over US$ 20 billion in 2008. Similarly, the Marine Stewardship \nCouncil (MSC) for certified sustainable seafood is estimated to have a value of US$ 1.5 \nBillion. MSC represents only 7% of the total global landings of marine fisheries (fish taken \nout of the water on to land), which is equivalent to 5.25 million tons of fish. These examples \nshow that while the numbers seem relatively low, the absolute impact is still very relevant, \nbecause of the scale, the scope and their value.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".