Comparative Life Cycle Assessment of Electrochemical Upgrading of CO2 to Fuels and Feedstocks
Bibliographic record
Abstract
<p>Development of electrochemical pathways to convert CO<sub>2</sub> into fuels and feedstock is rapidly progressing over the past decade. Here we present a comparative cradle-to-gate life cycle assessment (LCA) of one and two-step electrochemical conversion of CO<sub>2 </sub>to eight major value-added products; wherein we consider CO<sub>2</sub> capture, conversion and product separation in our process model. We measure the carbon intensity (i.e., global warming impact) of one and two-step electrochemical routes with its counterparts – thermochemical CO<sub>2</sub> utilization and fossil-fuel based conventional synthesis routes for those same products. Despite inevitable carbonate formation in one-step CO<sub>2</sub> electrolysis, this analysis reveals one-step electrosynthesis would be equally compelling (through the lens of climate benefits) as compared to two-step route. This analysis further reveals that the carbon intensity of electrosynthesis products is due to significant energy requirement for the conversion (70-80% for gas products) and product separation (40-85% for liquid products) phases. Electrochemical route is highly sensitive to the electricity emission factor and is compelling only when coupled with electricity with low emission intensity (<0.25 kg CO<sub>2</sub>e/kWh). As the technology advances, we identify the near-term products that would provide climate benefits over fossil-based routes, including syngas, ethylene and n-propanol. We further identify technological goals required for electrochemical route to be competitive, notably achieving liquid product concentration >20 wt%. It is our hope that this analysis will guide the CO<sub>2</sub> electrosynthesis community to target achieving these technological goals, such that when coupled with low-carbon electricity, electrochemical route would bring climate benefits in near future. </p>
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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) | 0.001 | 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".