Reforming agricultural support for improved environmental outcomes
Bibliographic record
Abstract
Agricultural support has changed substantially in both rich and poor countries in recent years. In rich countries, there has been a strong move to decoupled subsidies and a fall in average rates of protection. In developing countries, market price support remains the dominant form of protection and average rates of support have risen—breaking the traditional pattern of taxing agriculture. Emissions from agriculture and land use change have contributed up to a third of total greenhouse gas emissions, with beef, milk and rice production accounting for more than 80 percent of agricultural emissions. Agricultural support was biased against emission-intensive goods until recent years and is now only slightly biased towards them. Although emission intensities are relatively higher in the developing countries, they have fallen far more rapidly in developing countries than in the rich countries in the past quarter-century, as agricultural productivity has grown in developing countries. Policy reform will be challenging given the strong political-economy support for the current structure of protection. Increasing investments in research and development to raise productivity and lower the emissions intensity of agricultural output would help agriculture and the environment.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".