Intégration des paramètres spatio-temporels et des risques d'accident à l'Analyse du Cycle de Vie : Application à la filière hydrogène énergie et à la filière essence
Bibliographic record
Abstract
In the current context to preserve the environment, we chose to use the Life Cycle Assessment (LCA) to assess the environmental performances of energy systems for transport and in particular hydrogen energy whose been studied by the Group of Schools of Mines in the H2-PAC project. This LCA highlights the poor performances of the fuel cell manufacture of direct hydrogen and bioethanol-hydrogen patterns. This LCA also highlights the poor performances of gasoline and combustion hydrogen patterns related to the use of internal combustion engines. Because of the failure to take into account the spatial and temporal parameters in the characterization of local and regional environmental impacts, we have used the Site Dependent approach to develop a most relevant classification methodology based on two key points : the determination of the environmental concentration of the substance by the EUSES model and the determination of the relevance of the impact characterisation according to this concentration. We tested our new methodology of classification in order to reassess the local and regional impacts of three life cycle stages : the fuel cell manufacture, the use of gasoline and hydrogen engines. This reassessment has confirmed that the poor performances of the fuel cell manufacture but it questioned the strong contribution of emissions from the engines. The integration of spatial and temporal parameters for the assessment of these impacts provides a better understanding of the mobile emissions sources. On the sidelines of this environmental assessment, a risk analysis of direct hydrogen and gasoline patterns was realised under the dangerous image of hydrogen. This Life Cycle Risk Analysis shows that these two patterns present a similar risk even if the hydrogen storage seems problematic.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".