Responding to the debate on "food versus fuel" by substituting direct biofuel with precision agriculture incentives: a general equilibrium approach
Bibliographic record
Abstract
The Food Versus Fuel (FvF) debate antagonises biofuel production as it raises food prices. However, biofuels could reduce GHG emissions by up to 410-640 MtCO2eq per year in the USA and have already accounted for 11.4 $MtCO_2$ of avoided emissions in the Canadian transportation sector in 2023. Transportation represented 35\% and agriculture contributed 10\% to Canadian greenhouse gas (GHG) emissions in 2021, indicating the need to abate emissions in both sectors. Precision agriculture (PA) is a set of emissions-abating technologies that could present an alternative strategy to decarbonise the transportation sector through reducing biofuel production costs as a complement or substitute to electrification. We investigate whether PA adoption incentives increase biofuel production and use, lower food prices, and reduce agricultural and transportation emissions. Using Environment Canada’s Multi-Sector, Multi-Region (EC-MSMR) Computable General Equilibrium (CGE) model, we quantify the impact of PA incentives on biofuel usage in transportation by 2050. We find that a mix of cap-and-trade and emissions-pricing systems that reach a price of 692-940 $\frac{2017 CAD}{t}$ could incentivize PA and mediate the FvF debate. Under these policy simulations, Canadian consumption increases between 0.58\% and 1.34\%, crop production increases between 32\% and 42.81\%, food prices reduce between 1.47\% and 5.16\%, and Canadian crop production would become more competitive with PA by 2050. Transportation biofuel use increases by up to 23.5\% and CO2 emissions fall between 8.19\%-16.85\% by 2050. Finally, agricultural N2O emissions intensity decreases between 14.2\% and 14.41\%. Our study responds to SDG 7, 11, and 13 showing that PA incentives unlock emissions-abatement by increasing Canadian biofuel affordability while reducing food prices.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".