Feasibility of subsurface storage of hydrochar in the Netherlands as carbon dioxide removal technique
Bibliographic record
Abstract
Hydrothermal carbonisation enables the conversion of wet biomass into hydrochar, a carbon-rich solid with potential for durable carbon dioxide removal (CDR). While hydrochar has been studied extensively for topics as soil application or wastewater treatment, its role in subsurface storage remains underexplored. This study examines the feasibility of hydrochar-based biomass carbon removal and storage (BiCRS) in the Netherlands, where abundant wet biomass and well-developed subsurface infrastructure offer a promising deployment context. We characterise the chemical and mechanical properties of manure-derived hydrochar and evaluate seven potential storage configurations, from abandoned coal mines to quarry lakes and lightweight fill applications, based on technical feasibility, environmental risk, and long-term containment. Our findings identify two priority pathways: storage in salt caverns and use as lightweight filling material for land elevation. A third pathway, storage in sand quarry lakes, also holds potential, though additional safeguards and site-specific assessments are needed to ensure environmental integrity and carbon retention. Hydrochar’s compatibility with wet, low-value feedstocks and potential for decentralised implementation position it as a flexible addition to the CDR portfolio. However, realising this potential will depend on further field validation, material optimisation, and regulatory alignment. Key uncertainties remain regarding long-term degradation, leachate behaviour, and performance under representative subsurface conditions. This study highlights hydrochar as a scalable, technically viable CDR approach. If supported by robust containment, monitoring, and governance frameworks, it could play a meaningful role in national and regional climate mitigation strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".