Is it possible to reduce greenhouse gas emissions without reducing production? An assessment of 26 technical options
Bibliographic record
Abstract
In Europe, agriculture is responsible for 10.2% of greenhouse gas (GHG) emissions. The objective of this study was to assess technical measures to reduce GHG emissions at the farm level without reducing production outputs. France was chosen as a case study with a typical intensive and diversified agriculture. Ten measures, split into 26 sub-measures, were selected from an initial list of 100 “candidate” measures. The selection process was based on five criteria: the expected effect on production, the GHG abatement potential, the current availability of the technology required to implement the measure, the applicability of the measure, including its social acceptability, and the potential synergies or antagonisms with other agri-environmental objectives, including adaptation to climate change. The ten selected measures were linked to nitrogen management, management practices which increase carbon storage in soils and biomass, livestock diets and energy production and consumption on farms. Their abatement potential and cost were accurately calculated and compared, using a marginal abatement cost curve approach. Results show that one third of the cumulated abatement potential corresponds to sub-measures with a negative cost. These sub-measures are based on an improved efficiency of inputs like N fertilizers, animal feed and energy, with no negative effect on production. Moreover, no antagonism with the objective of adaptation exists for these sub-measures. Other sub-measures are characterised by a higher cost, because of specific investments, the purchase of specific inputs or dedicated labour time, sometimes partially compensated by additional marketable products (biogas, wood…). Among the 26 sub-measures, only four exhibit a slight antagonism with the objective of adaptation to climate change. When calculated under current inventory rules, the overall annual abatement represents 10% of annual emissions from agriculture. This percentage is higher when calculated using higher tiers. It is concluded that cost-effective technical levers exist for agriculture to support greenhouse gas mitigation without hampering production and adaptation goals.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".