The System Value of Deploying Bioenergy with CCS (BECCS) in the United Kingdom
Bibliographic record
Abstract
Bioenergy with CCS (BECCS) is a credible, scalable and efficient technology, and its deployment is critical in order for the United Kingdom to meet its 2050 GHG emissions reduction targets cost–effectively. Major advances in the fundamental science and technology development have been made by the ETI and others over the last 10 years – significantly de-risking this value chain, and evidencing that there are no ‘show-stopping’ technical barriers to BECCS. Specifically, advances have been made in understanding the costs, efficiencies and challenges of biomass-fed combustion systems with carbon capture; the evidence that numerous bioenergy value chains can deliver significant carbon savings; the sizeable negative emissions potential when bioenergy is combined with CCS, based on certain feedstocks; the potential availability and sustainability of feedstocks relevant to the United Kingdom and the identification and assessment of high-capacity, low-cost, low-risk stores for CO2 around the United Kingdom and the infrastructure required to connect to them. Analyses show that the United Kingdom is exceptionally well placed to exploit the benefits of BECCS, given its vast offshore storage opportunities, its experience in bioenergy deployment and UK academic and industrial research and development strength across bioenergy and CCS. A consistent biomass feedstock planting rate of 30 000 ha per annum, combined with moderate imports, is sufficient to keep the United Kingdom on the required trajectory for meeting the 2050 bioenergy and negative emissions targets. Given these advances in understanding and de-risking, it should now be an integral part of the United Kingdom's future CCS strategy. BECCS deployment is achievable by 2030, since all major components of a BECCS system have now been demonstrated or proven individually – significantly de-risking full-system deployment. Great progress is being made in the United Kingdom and internationally on the operational and handling aspects of biomass combustion, co-firing and CCS, through ‘learning by doing’ in pilot research trials and full-scale plant demonstrations, e.g. Drax's coal unit conversions to biomass in the United Kingdom and the Boundary Dam commercial-scale coal power CCS project in Canada. Significant support is needed over the next 5 to 10 years to demonstrate commercial deployment of BECCS technology and the wider biomass and CO2 storage supply chain in the United Kingdom.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".