RED Versus REDD: The Battle Between Extending Agricultural Land Use and Protecting Forest
Bibliographic record
Abstract
This paper analyses the complex battle between RED and REDD policies and the resulting global consequences on land use, agricultural production, international trade flows and world food prices. A key methodological challenge is the representation of land use and the possibility to convert forestry land into agricultural land as REDD policies might prevent the use of forestry and wood lands for agriculture. The paper introduces a flexible land supply function allowing large changes in the total potential land availability for agriculture due to environmental considerations such as reducing emissions from deforestation. The parameters of the new land supply function are defined as variables of the model. In the paper, we simplify the implementation of the REDD policies as a shift in potential availability for agricultural land in various regions in the world. Both analysed policies are designed to save emissions but their land use impacts are opposite. The paper shows that global RED policies expand global land use with 3% relative to the baseline. Land abundant countries such as Canada, USA and Indonesia extend their use of agricultural land and their agricultural production. Severe REDD policies that protect all forest and woodlands in especially tropical land abundant regions such as Central and South America, South Africa and Indonesia imply a global reduction of agricultural land by 5% and lead to higher food and land prices. REDD policies reverse production and trade patterns as previous land abundant countries become land scarce countries.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".