A comprehensive review of life cycle assessments of direct air capture and carbon dioxide storage
Bibliographic record
Abstract
This review critically assesses Life Cycle Assessments (LCAs) of Direct Air Capture and Carbon Storage (DACCS) technologies, emphasizing environmental impact and effectiveness of these technologies. As global efforts to mitigate CO₂ emissions intensify, DACCS is increasingly viewed as a promising solution, yet its broader environmental implications require careful consideration. The review synthesizes findings from various LCA studies, revealing substantial variability in life cycle efficiency and environmental impacts across different DACCS systems. Solid sorbent technologies demonstrate average net greenhouse gas reductions of 640 kg CO₂-eq/t CO₂, while liquid sorbent systems achieve reductions of about 560 kg CO₂-eq/t CO₂, with system carbon efficiencies ranging between 56 % and 64 %, influenced by operational conditions and regional factors. Beyond climate impacts, DACCS systems exhibit significant resource demands: water consumption ranges from 1 to 12 tons per ton of CO 2 captured, and land use spans 85–4450 km 2 based on system configuration and renewable energy requirements. For gigaton-scale facilities, significant environmental trade-offs emerge, including substantial particulate matter emissions (170–180 kt annually) and varying impacts on marine eutrophication (up to 90 % higher for amine-based systems compared to hydroxide-based alternatives). Low-temperature DAC systems exhibit higher human toxicity and ecotoxicity impacts due to increased electricity demands, while metal resource depletion varies significantly based on system design and energy sources. This study highlights the critical need for standardized LCAs and transparent reporting practices to enable consistent comparisons between technologies. Based on the analysis, the review provides recommendations for optimizing system design and deployment strategies to minimize environmental trade-offs while maximizing carbon removal potential. These insights support efforts to achieve carbon neutrality by 2050 in alignment with Intergovernmental Panel on Climate Change (IPCC) targets.
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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) | 0.002 | 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".