Development of a Framework for the Assessment of Energy Demand-Based Greenhouse Gas Mitigation Options for the Agriculture Sector
Bibliographic record
Abstract
Abstract. This study assesses greenhouse gas (GHG) mitigation options for the agriculture sector. The Long-range Energy Alternatives Planning (LEAP) model was used to develop a framework to assess future trends in energy demand and associated GHG emissions for the agriculture sector and to assess various GHG mitigation options associated with energy consumption. A business-as-usual (reference) scenario and 32 GHG mitigation scenarios were developed for the years 2009-2050 using the LEAP model. A case study for Alberta, Canada, was conducted. In the model, GHG mitigation scenarios were developed for the energy demand side (e.g., farm machines, farm transportation, lighting, and ventilation) based on efficiency improvements and the use of renewable energy. The mitigation scenarios were divided into two planning horizons based on technology penetration: slow penetration (2009-2050) and fast penetration (2009-2030). For each planning horizon, 16 scenarios were assessed. Of all farm machines, efficient diesel tractors have the highest GHG mitigation potential: 12.35 MT of CO2 equivalent by 2050 and 4.7 MT of CO2 equivalent by 2030. In addition, GHG abatement cost curves show that biodiesel tractors and efficient diesel tractors have the highest GHG mitigation potential, with attractive abatement costs of -$62 and -$11 tonne-1 of CO2 mitigated by 2050, respectively. Keywords: Abatement cost, Agriculture sector, Energy efficiency, GHG mitigation, LEAP model.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".