Considering Time in LCA: Dynamic LCA and Its Application to Global Warming Impact Assessments
Bibliographic record
Abstract
The lack of temporal information is an important limitation of life cycle assessment (LCA). A dynamic LCA approach is proposed to improve the accuracy of LCA by addressing the inconsistency of temporal assessment. This approach consists of first computing a dynamic life cycle inventory (LCI), considering the temporal profile of emissions. Then, time-dependent characterization factors are calculated to assess the dynamic LCI in real-time impact scores for any given time horizon. Although generally applicable to any impact category, this approach is developed here for global warming, based on the radiative forcing concept. This case study demonstrates that the use of global warming potentials for a given time horizon to characterize greenhouse gas emissions leads to an inconsistency between the time frame chosen for the analysis and the time period covered by the LCA results. Dynamic LCA is applied to the US EPA LCA on renewable fuels, which compares the life cycle greenhouse gas emissions of different biofuels with fossil fuels including land-use change emissions. The comparison of the results obtained with both traditional and dynamic LCA approaches shows that the difference can be important enough to change the conclusions on whether or not a biofuel meets some given global warming reduction targets.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".