RED Versus REDD: The Battle Between Extending Agricultural Land Use and Protecting Forest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it