Evaluating the economic feasibility of replacing propane with solid biofuels for grain drying – a scenario analysis
Bibliographic record
Abstract
Abstract This study investigates the economic feasibility of replacing propane with biomass (solid biofuels) for grain drying under four scenarios. Two scenarios are based on the recently increased price of propane and the possible future changes in prices for propane and biomass. The other scenarios assess the impact of two policies on the cost of energy when carbon pricing is included and a financial incentive program is designed to share the purchase and installation cost of clean technologies such as biomass burners. A farm business in Canada is used as the case study to compare and contrast the energy cost of drying wheat grain over the range of 160 000–400 000 bushels (4355–10 890 t) using wood chip or wood pellet burners instead of propane. The results indicate that, under all scenarios, biomass burners are a more economic option than propane burners for drying grain. In the baseline scenario (no carbon pricing/financial support), cost reductions of 34–62% can be achieved by using a wood chip burner instead of a propane burner. This reduction is estimated to be 52–58% for wood pellet burners versus the propane burner. Every $10tCO2e‐1 increase in the carbon pricing adds about 1.7% to the total drying cost for the propane burner. This further increases the cost saving of switching from propane to biomass, as no carbon pricing is applied to biomass as a renewable energy source. A 50% cost sharing arrangement as a financial support to purchase and install biomass burners can provide a further 2–7% cost saving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".