The Need for and Path to Harmonized Life Cycle Assessment and Techno‐Economic Assessment for Carbon Dioxide Capture and Utilization
Bibliographic record
Abstract
The use of carbon dioxide as a feedstock for a broad range of products can help mitigate the effects of climate change through long‐term removal of carbon or as part of a circular carbon economy. Research on capture and conversion technologies has intensified in recent years, and the interest in deploying these technologies is growing fast. However, sound understanding of the environmental and economic impacts of these technologies is required to drive fast deployment and avoid unintended consequences. Life cycle assessments (LCAs) and techno‐economic assessments (TEAs) are useful tools to quantify environmental and economic metrics; however, these tools can be very flexible in how they are applied, with the potential to produce significantly different results depending on how the boundaries and assumptions are defined. Built on ISO standards for generic LCAs, several guidance documents have emerged recently from the Global CO 2 Initiative, the National Energy Technology Laboratory, and the National Renewable Energy Laboratory that further define assessment specifications for carbon capture and utilization. Overall agreement in the approaches is noted with differences largely based on the intended use cases. However, further guidance is needed for assessments of early‐stage technologies, reporting details, and reporting for policymakers and nontechnical decision‐makers.
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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 | 0.118 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".