Estimated climate impact of replacing agriculture as the primary food production system
Bibliographic record
Abstract
Abstract Global agriculture is the second largest contributor to anthropogenic climate change after the burning of fossil fuels. However the potential to mitigate the agricultural climate change contribution is limited and must account for the imperative to supply food for the global population. Advances in microbial biomass cultivation technology have recently opened a pathway to growing substantial amounts of food for humans or livestock on a small fraction of the land presently used for agriculture. Here we investigate the potential climate change impacts of the end of agriculture as the primary human food production system. We find that replacing agricultural primary production with electrically powered microbial primary production before a low-carbon energy transition has been completed could redirect renewable energy away from replacing fossil fuels, potentially leading to higher total CO 2 emissions. If deployed after a transition to renewable energy, the technology could alleviate agriculturally driven climate change. These diverging pathways originate from the reversibility of agricultural driven global warming and the irreversibility of fossil-fuel CO 2 driven warming. The range of reduced warming from the replacement of agriculture ranges from −0.22 (−0.29 to −0.04) ∘ C for shared socioeconomic pathway (SSP)1 −1.9 to −0.85 (−0.99 to −0.39) ∘ C for SSP4-6.0. For limited temperature target overshoot scenarios, replacement of agriculture could eliminate or reduce the need for active atmospheric CO 2 removal to achieve the necessary peak and decline in global warming.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".