Scenario assessment of introducing carbon utilization and carbon removal technologies considering future technological transition based on renewable energy and direct air capture
Bibliographic record
Abstract
Carbon capture and utilization (CCU) and carbon dioxide removal (CDR) technologies have the potential to significantly contribute to GHG reduction. Numerous studies have evaluated the CO2 reduction effects and economics of CCUs and CDRs; however, uncertainties in these evaluations due to various regional characterizations and future technological transitions are of high importance. In this study, four synthetic fuels (fuels produced from captured CO2 and H2), methanol, methane, gasoline, and diesel, were evaluated by life cycle assessment (LCA) and techno-economic assessment (TEA). Five representative countries with different regional characteristics were selected for the study. A bottom-up integrated LCA/TEA approach was used, involving detailed process simulations to avoid uncertainties and to evaluate future technological transitions based on direct air capture (DAC) and renewable energy. The overarching objective of this study was to provide a transparent framework with a common dataset generation methodology to determine the countries that have the advantages/disadvantages in synthetic fuel production and sale, considering the local operational parameters and large-scale introduction of DAC/renewable energy. Such multiple synthetic fuel analyses across various jurisdictions have not been conducted in previous studies. The results showed that the highest and lowest CO2 reductions were achieved by diesel (average 6.33 kg-CO2·kg−1) and methanol (average 3.43 kg-CO2·kg−1), respectively, whereas the highest and lowest product costs were of gasoline (average 2.96 USD·kg−1) and methanol (average 0.82 USD·kg−1), respectively. The technological transition using DAC and renewable energy showed average CO2 emission reductions of 47% (methanol), 99% (methane), 545% (gasoline), and 621% (diesel). Moreover, in the future, the lowest CO2 emission reduction costs are expected in Germany for methanol and diesel, Australia for methane, and Canada for gasoline. These findings can contribute to improving international collaboration to promote CCU and CDR technologies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".