A New Perspective for Climate Change Mitigation—Introducing Carbon-Negative Hydrogen Production from Biomass with Carbon Capture and Storage (HyBECCS)
Bibliographic record
Abstract
The greatest lever for advancing climate adaptation and mitigation is the defossilization of energy systems. A key opportunity to replace fossil fuels across sectors is the use of renewable hydrogen. In this context, the main political and social push is currently on climate neutral hydrogen (H2) production through electrolysis using renewable electricity. Another climate neutral possibility that has recently gained importance is biohydrogen production from biogenic residual and waste materials. This paper introduces for the first time a novel concept for the production of hydrogen with net negative emissions. The derived concept combines biohydrogen production using biotechnological or thermochemical processes with carbon dioxide (CO2) capture and storage. Various process combinations referred to this basic approach are defined as HyBECCS (Hydrogen Bioenergy with Carbon Capture and Storage) and described in this paper. The technical principles and resulting advantages of the novel concept are systematically derived and compared with other Negative Emission Technologies (NET). These include the high concentration and purity of the CO2 to be captured compared to Direct Air Carbon Capture (DAC) and Post-combustion Carbon Capture (PCC) as well as the emission-free use of hydrogen resulting in a higher possible CO2 capture rate compared to hydrocarbon-based biofuels generated with Bioenergy with Carbon Capture and Storage (BECCS) technologies. Further, the role of carbon-negative hydrogen in future energy systems is analyzed, taking into account key societal and technological drivers against the background of climate adaptation and mitigation. For this purpose, taking the example of the Federal Republic of Germany, the ecological impacts are estimated, and an economic assessment is made. For the production and use of carbon-negative hydrogen, a saving potential of 8.49–17.06 MtCO2,eq/a is estimated for the year 2030 in Germany. The production costs for carbon-negative hydrogen would have to be below 4.30 € per kg in a worst-case scenario and below 10.44 € in a best-case scenario in order to be competitive in Germany, taking into account hydrogen market forecasts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".