An Approach to Identify the Suitable Plant Location for Miscanthus-Based Ethanol Industry: A Case Study in Ontario, Canada
Bibliographic record
Abstract
The life cycle (LC) of ethanol extracted from Miscanthus has been evaluated to identify the potential location for the Miscanthus-based ethanol industry in Ontario, Canada to mitigate greenhouse gas (GHG) emissions and minimize the production cost of ethanol. Four scenarios are established considering the land classes, land use, and cropping patterns in Ontario, Canada. The net energy consumption, emissions, and cost of ethanol are observed to be dependent on the processing plant location and scenarios. The net energy consumption, emissions, and cost vary from 12.9 MJ/L to 13.4 MJ/L, 0.79 $/L to 0.84 $/L, and 0.45 kg-CO2e/L to 1.32 kg-CO2e/L, respectively, which are reliant on the scenarios. Eastern Ontario has emerged as the best option. This study reveals that Miscanthus is a potential feedstock for the ethanol industries in Ontario, even if it is cultivated on marginal land. This study also highlights the contribution of energy crops (Miscanthus) to avoid the potential technical and economic constraints of lignocellulosic biomass for the renewable energy industry. Miscanthus may help avoid competition with food crops for prime land (higher quality land that is suitable for food crops), avoid the food versus fuel debate, help meet the ethanol demand, and achieve the GHG emissions abatement target of Canada.
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 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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".