The performance of solvent-based direct air capture across geospatial and temporal climate regimes
Bibliographic record
Abstract
Introduction Liquid-solvent direct air capture (DAC) is a prominent approach for carbon dioxide removal but knowing where to site these systems is challenging because it requires considering a multitude of interrelated geospatial factors. Two of the most pressing factors are: (1) how should DAC be powered to provide the greatest net removal of CO 2 and (2) how does weather impact its performance?. Methods To investigate these questions, this study develops a process-level model of a liquid-solvent DAC system and couples it to a 20-year dataset of temperature and humidity conditions at a ~9km resolution across the contiguous US. Results and discussion We find that the amount of CO 2 sequestered could be 30% to 50% greater than the amount of CO 2 removed from the atmosphere if natural gas is burned on site to power DAC, but that the optimal way to power DAC is independent of capture rate (i.e., weather), depending solely on the upstream GHG intensity of electricity and natural gas. Regardless of how it is powered, air temperature and humidity conditions can change the performance of DAC by up to ~3x and can also vary substantially across weather years. Across the continuous US, we find that southern states (e.g., Gulf Coast) are preferrable locations for a variety of reasons, including higher and less variable air temperature and relative humidity. Lastly, we also find the performance of liquid-solvent DAC calculated with monthly means is within 2% of the estimated performance calculated with hourly data for more than a third of the country, including in the states with weather most favorable for liquid-solvent DAC.
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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 | 0.000 |
| 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".