Coupled social and land use dynamics affect dietary choice and agricultural land-use extent
Bibliographic record
Abstract
Abstract Dietary patterns have long been a driver of global land use. Increasingly, they also respond to it, in part because of social processes that support adoption of eco-conscious diets. Here we develop a coupled social-and-land use mathematical model parameterised for 153 countries. We project global land use for future population, income, and agricultural yield using our coupled dynamical model. We find that coupled social-and-land feedbacks can alter the peak global land use for agriculture by up to 2 billion hectares, depending on the parameter regime. Across all yield scenarios, the model projects that social dynamics will cause an increase in eco-conscious dietary behaviour until the middle of the 21st century, after which it will decline in response to declining land use caused by a shrinking global population. The model also exhibits a regime of synergistic effects whereby simultaneous changes to multiple socio-economic parameters are required to change land use projections. This research demonstrates the value of including coupled social-and-land feedbacks in land use projections.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".