Carbon footprinting of carbon capture and -utilization technologies: discussion of the analysis of Carbon XPRIZE competition team finalists
Bibliographic record
Abstract
Abstract Life cycle assessments (LCAs) of early-stage technologies can provide valuable insights about key drivers of emissions and aid in prioritizing research into further emissions-reduction opportunities. Despite this potential value, further development of LCA methods is required to handle the increased uncertainty, data gaps, and confidentially of early-stage data. This study presents a discussion of the life cycle carbon footprinting of technologies competing in the final round of the NRG COSIA Carbon XPRIZE competition—a US$20 million competition for teams to demonstrate the conversion of CO2 into valuable products at the scale of a small industrial pilot using consistent deployment conditions, boundaries, and methodological assumptions. This competition allowed the exploration of how LCA can be used and further improved when assessing disparate and early-stage technologies. Carbon intensity estimates are presented for two conversion pathways: (i) CO2 mineralization and (ii) catalytic conversion (including thermochemical, electrochemical, photocatalytic and hybrid process) of CO2, aggregated across teams to highlight the range of emissions intensities demonstrated at the pilot for individual life cycle stages. A future scenario is also presented, demonstrating the incremental technology and deployment conditions that would enable a team to become carbon-avoiding relative to an incumbent process (i.e. reducing emissions relative to a reference pathway producing a comparable product). By considering the assessment process across a diverse set of teams, conversion pathways and products, the study presents generalized insights about opportunities and challenges facing carbon capture and -utilization technologies in their next phases of deployment from a life cycle perspective.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".