A Method of Multi-Environmental Spatial Scales Division in LCA Based on Chinese Region-Specific
Bibliographic record
Abstract
Traditionally LCA generally focus on a global scale and on steady-state, linear modeling. How to reflect the varying and complicated spatial characteristics of environmental impacts of large numbers of processes is in urgent need for improving the practicality of the LCA, especially for that with distinct environmental diversity in China. Based on the Chinese environmental policies and standards, a method of multi-environmental spatial scales division in LCA is proposed. First, environmental space types are discussed according to the relations between the life cycle inventories, impact categories of LCA and the environmental spatial characteristics. Second, the environmental spatial scale coefficient is proposed and defined in order to quantify the spatial characteristics and used in the calculation for the environmental impact potentials of products. The method of calculation for the coefficient is presented by analyzing the endure capacity of regional environment. Finally, this method is applied to study the spatial characteristics of ambient air quality and other environmental space types.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".