Estimated climate impact of the end of agriculture as the primary food production system
Bibliographic record
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 contribution is limited by 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, by fuelling microbial growth with hydrogen produced from electrolysis powered by renewable energy. This method of food production would use 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 microbial biomass cultivation technology has both the potential to exacerbate climate change by outcompeting economic decarbonization for renewable energy and the potential to mitigate climate change if deployed following economic decarbonization. A duality which originates from the contrast between the reversibility of agricultural driven climate change and the irreversibility of fossil-fuel CO2 driven climate change. The range of reduced warming from the replacement of agriculture ranges from -0.22 [-0.29 to -0.04]oC for Shared Socioeconomic Pathway (SSP) 1-1.9 to -0.85 [-0.99 to -0.39]oC for SSP4-6.0. For limited temperature target overshoot scenarios, replacement of agriculture could thus eliminate or reduce the need for active atmospheric CO2 removal to achieve the necessary peak and decline in global warming. Given current societal barriers to switching to a microbial-based diet, deep near-term emissions reductions in CO2 and agricultural emissions remain necessary steps to keep warming within the bounds set by the Paris Agreement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".