Evaluating microalgae‐to‐energy ‐systems: different approaches to life cycle assessment (<scp>LCA</scp>) studies
Bibliographic record
Abstract
Abstract Life cycle assessment ( LCA ) is a valuable tool for determining the environmental impacts associated with different products and has been widely used to assess biofuel production. As a scientific methodology rather than a standardized test, every LCA may be thought of as unique in terms of the selection of functional units or determination of system boundaries. Researchers generally tailor the method to meet the specific goals of their own investigations. This review examines a number of LCAs used to evaluate microalgae‐to‐energy systems, and evaluates their contributions in terms of their ability to support commercialization efforts in this sector. To this end, a new scoring system for LCAs is proposed based on input/output flows, data origin, production technologies and system boundaries, selection of assumptions and variables, as well as the ability to track environmental, economic, and social impacts. The review suggests that, while a wide variety of new technological pathways for microalgae‐to‐energy systems are being assessed, the majority of studies reported employ relatively limited system boundaries that may not capture the full impacts of the processes. The number of environmental impact factors being tracked is limited, and many studies do not consider important impacts such as water or land use. Most studies do not incorporate critical information about economics related to new process configurations, which will be essential to support commercialization efforts in this area. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".