Techno-economic and environmental analysis of gasification of waste or forest biomass into hydrogen in remote communities using the Northwest Territories as a case study
Bibliographic record
Abstract
Transportation fuels such as diesel and gasoline contribute to 61 % of the total emissions in the Northwest Territories (NWT) and are expensive at $56.92–60.88/GJ, which is twice the cost of that in most of Canada. In this study, municipal solid waste, mixed plastics, tire waste, and forest biomass are assessed for the production of 14 t/day hydrogen using gasification to replace transportation fuels. While hydrogen reduces the emissions from diesel and gasoline by 99 %, the cost of hydrogen from forest biomass is significantly higher at $19.09/kg ($134.44/GJ). The cost is lower with feedstocks of mixed plastics and tire waste - $15.80/kg ($111.26/GJ) and $13.75/kg ($96.83/GJ), considering tipping fees - but there are insufficient amounts of these wastes. Even with a 40 % Clean Hydrogen Investment credit on the production cost of hydrogen, this cost for hydrogen is more than double the average cost of diesel and gasoline in the NWT. • Waste sources limited to forestry biomass to meet diesel and gasoline needs in NWT. • H 2 cuts transportation emissions by 99 %, but costs twice as much as current fuel. • LCOH from mixed plastics and tires is $14–23/GJ less than from forest biomass. • CO 2 Transport & Storage costs are prohibitive in remote communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".