Evaluating the Sustainability of Producing Hydrogen from Biomass through Well-to-Wheel Analyses
Bibliographic record
Abstract
<div class="htmlview paragraph">Hydrogen is viewed by many as the most promising fuel for light-duty vehicles (LDVs) for the future. Hydrogen can be produced through a large number of pathways and from many feedstocks (both fossil and renewable). A key issue in evaluating the sustainability of a hydrogen fuel cell vehicle (FCV) is an analysis of the processes employed to produce the hydrogen and the efficiency of its use in the vehicle, the ‘well-to-wheel’ (WTW) activities.</div> <div class="htmlview paragraph">Several recent WTW studies, which include conventional as well as alternative fuel/propulsion system LDVs, are examined and compared. One potentially attractive renewable feedstock for hydrogen is biomass. A biomass to hydrogen pathway and its use in a FCV has only recently been included in WTW studies. The analysis is based on those studies which include biomass-derived hydrogen, comparing it to gasoline/diesel internal combustion engine vehicles (ICEVs) and FCV which utilize hydrogen from other feedstocks (natural gas and wind-generated electricity).</div> <div class="htmlview paragraph">Since hydrogen is not commercially produced from biomass, all of these studies utilize process and emissions data based on research results and extrapolations to commercial scale. We find that direct comparison of results between studies is challenging due to the differences in study methodologies and assumptions concerning feedstocks, production processes, and vehicles. Overall however, WTW results indicate that hydrogen produced via the gasification of biomass and its use in a FCV has the potential to reduce greenhouse gas (GHG) emissions by between 75% and 100% and utilize little fossil energy compared to conventional gasoline ICEVs and hydrogen from natural gas FCVs.</div>
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".