Liquid solvent direct air capture’s cost and carbon dioxide removal vary with ambient environmental conditions
Bibliographic record
Abstract
Emission trajectories produced by integrated assessment models increasingly suggest that gigatonnes of carbon removal will be required to stabilize atmospheric greenhouse gas concentrations at safe levels. This can be accomplished using the direct air capture of carbon dioxide, among other technologies. Process models of these systems assume that they would operate at standard ambient temperature and pressure, when capture rates vary with ambient conditions, including temperature, relative humidity, and other factors. Here, we build an open-source model of a liquid solvent direct air capture technology and analyze its capture performance as a function of hourly varying ambient environmental conditions across Canada. We find that, in the cool climate considered, capture performance is degraded due to both varying environmental conditions and the intermittent operation that could result. Our findings can be used to calibrate policy and investment decisions, and to support engineers in making operational design choices. In Canada, the performance of liquid-solvent direct air capture plants degrades in cold climates, while in warm climates, the performance is boosted but requires increased use of water, according to an analysis that uses an open-source model that combines chemical processes and cost data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".