International Willingness to Pay for the Protection of the Amazon Rainforest
Bibliographic record
Abstract
The Amazon rainforest, the world's \n largest tropical rainforest and an important constituent of \n the global biosphere, continues degrading by rapid \n deforestation, which is expected to continue despite \n policies to prevent it. Current international funding to \n protect the Amazon rainforest focuses on benefits from \n reduced carbon emissions. This paper examines an additional \n rationale for Amazon protection: the valuation of its \n biodiversity and forests as natural heritage to the \n international community. To measure the economic value of \n this benefit, the paper examines U.S. and Canadian \n households' willingness to pay to help finance Amazon \n rainforest protection. The analysis finds that mean \n willingness to pay to avoid forest losses projected to occur \n by 2050 despite current protective policies is $92 per \n household per year. Aggregating across all households and \n considering the area protected, the analysis finds that \n preserving the Amazon rainforest is worth $3,168 per hectare \n (95-percent confidence interval $1,580-$4,756), on average, \n to households in the United States and Canada. Considering \n households in other developed countries would generate yet \n larger estimates of aggregate value, likely comparable to \n the carbon benefits from rainforest protection. The results \n reveal high values of the Amazon rainforest to people \n geographically distanced from it, lending support to \n international efforts to reduce deforestation in the Amazon.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".