Current status and pillars of direct air capture technologies
Bibliographic record
Abstract
Climate change calls for adaptation of negative emission technologies such as direct air capture (DAC) of carbon dioxide (CO 2 ) to lower the global warming impacts of greenhouse gases. Recently, elevated global interests to the DAC technologies prompted implementation of new tax credits and new policies worldwide that motivated the existing DAC companies and prompted the startup boom. There are presently 19 DAC plants operating worldwide, capturing more than 0.01 Mt CO 2 /year. DAC active plants capturing in average 10,000 tons of CO 2 annually are still in their infancy and are expensive. DAC technologies still need to improve in three areas: 1) Contactor, 2) Sorbent, and 3) Regeneration to drive down the costs. Technology-based economic development in all three areas are required to achieve <$100/ton of CO 2 which makes DAC economically viable. Current DAC cost is about 2–6 times higher than the desired cost and depends highly on the source of energy used. In this review, we present the current status of commercial DAC technologies and elucidate the five pillars of technology including capture technologies, their energy demand, final costs, environmental impacts, and political support. We explain processing steps for liquid and solid carbon capture technologies and indicate their specific energy requirements. DAC capital and operational cost based on plant power energy sources, land and water needs of DAC are discussed in detail. At 0.01 Mt CO 2 /year capture capacity, DAC alone faces a challenge to meet the rates of carbon capture described in the goals of the Paris Agreement with 1.5–2°C of global warming. However, DAC may partially help to offset difficult to avoid annual emissions from concrete (∼8%), transportation (∼24%), iron-steel industry (∼11%), and wildfires (∼0.8%).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".