Life cycle assessment of priority biochemicals: A review and meta-regression analysis
Bibliographic record
Abstract
Compared to traditional petrochemicals, biochemicals have the potential to reduce greenhouse gas (GHG) emissions and energy consumption throughout their life cycle. Life cycle assessment (LCA) has been widely used to assess the potential environmental impacts of biochemicals. However, the diversity in biorefinery configurations (e.g., the choice of feedstocks, platforms, and conversion processes) and LCA modeling assumptions (e.g., functional units, system boundary, and allocation methods) all affect the estimated environmental impact results of biochemicals, leading to large uncertainties in understanding their environmental benefits. Our research provides a comprehensive review of the current refinery routes and their associated climate change impacts for 17 priority biochemicals from published LCA studies. We collected their Global Warming Potential (GWP) results and employed a system harmonization approach to minimize variations in LCA modeling assumptions. The results showed that most biochemicals exhibited lower GWP results compared to their petrochemical counterparts, mainly due to the carbon sequestration credit through biomass growth and reduced GHG emissions during the chemical manufacturing processes. In addition, our meta-regression analysis showed that the variation in biorefinery feedstock types was the primary contributor to the variability in biochemical GWP results.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".