Carbon‐negative hydrogen production: Fundamentals for a techno‐economic and environmental assessment of HyBECCS approaches
Bibliographic record
Abstract
Abstract In order to achieve greenhouse gas neutrality, hydrogen generated from renewable sources will play an important role. Additionally, as underlined in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), new technologies to remove greenhouse gases from the atmosphere are required on a large scale. A novel concept for hydrogen production with net negative emissions referred to as HyBECCS (Hydrogen Bioenergy with Carbon Capture and Storage) combines these two purposes in one technological approach. The HyBECCS concept combines biohydrogen production from biomass with the capture and storage of biogenic carbon dioxide. Various technology combinations of HyBECCS processes are possible, whose ecological effects and economic viability need to be analyzed in order to provide a basis for comparison and decision‐making. This paper presents fundamentals for the techno‐economic and environmental evaluation of HyBECCS approaches. Transferable frameworks on system boundaries as well as emission, cost, and revenue streams are defined and specifics for the application of existing assessment methods are elaborated. In addition, peculiarities concerning the HyBECCS approach with respect to political regulatory measures and interrelationships between economics and ecology are outlined. Based on these considerations, two key performance indicators (KPIs) are established, referred to as levelized cost of carbon‐negative hydrogen (LCCNH) and of negative emissions (LCNE). Both KPIs allow deciding whether a specific HyBECCS project is economically viable and allows its comparison with different hydrogen, energy provision, or negative emission technologies (NETs).
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".