Ecosystem Service Benefits of Lab-cultured and Insect Meat
Bibliographic record
Abstract
Background: Population and income growth are expected to augment meat demand, and consequently, the conversion of natural ecosystems into pasture. Promising alternatives to livestock, particularly lab-cultured and insect meats, use about 1% as much land. Utilizing these technologies could reduce pasture expansion and maintain natural ecosystem service values. This paper investigates: what is the value of the ecosystem services potentially maintained by reducing agricultural expansion through the adoption of cultured and insect meat? Methods: Total global livestock-associated agricultural expansion by 2050 was predicted using FAO livestock projections (1) multiplied by the average land-use per kilogram of meat (2) yielding 194Mha. This expansion was partitioned among ecosystems according to threat scores derived from past expansion (3). Changes to annual ecosystem service values were calculated using average global values from Costanza et al. (4) multiplied by predicted expansion per ecosystem. Results & Conclusion: Tropical forests and east-Asia were the most threatened ecosystem and region, respectively, by both area and value. The net loss in annual ecosystem service values in 2050 due to predicted livestock-associated agricultural expansion was calculated to be $732bn/yr, translating to a NPV of $6.62tn to 2050. The potential to save such large ecosystem services value justifies increased research and promotion of these protein production methods. Limitations: This research does not identify exact ecosystems that are both targeted by agricultural meat expansion and that yield large ecosystem benefits because it is not sufficiently spatially explicit. Thus, it should not be used as a reference for new ecosystem conservation zones.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".