Comparative Life Cycle Assessment of CO<sub>2</sub> Electrosynthesis to Fuels and Feedstocks
Bibliographic record
Abstract
Development of electrochemical pathways to convert CO2 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 CO2 to eight major value-added products; wherein we consider CO2 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 CO2 utilization and fossil-fuel based incumbent synthesis routes for those products. Due to inevitable carbonate formation or CO2 crossover in one-step CO2 electrolysis using neutral pH membrane electrode assembly, the two-step electrosynthesis pathways (CO2 to CO in solid oxide electrolysis cell followed by CO electroreduction in alkaline flow cell) would be distinctively compelling through the lens of climate benefits. This analysis further reveals that the carbon intensity of electrosynthesis products is due to significant energy requirement for conversion (75–85% of total energy consumption for gas products) and product separation (30–85% of total energy consumption for liquid products) phases. Electrochemical route is highly sensitive to the electricity emission intensity and is compelling only when coupled with low emission intensity (<0.25 kg CO2e per kWh). As the technology advances, we identify near-term compelling products that would provide climate benefits over incumbent 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 CO2 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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".