CARBON FOOTPRINT ACCOUNTING AND EVALUATION OF AUTOMOTIVE AIR CONDITIONING FILTERS BASED ON LIFE CYCLE ASSESSMENT (LCA)
Bibliographic record
Abstract
Driven by the "dual carbon" goals to promote the green transformation of the automotive industry, the full-life-cycle carbon footprint of automotive components has become a core focus of the industry's low-carbon development. This study takes automotive air conditioning filters as the research object. Based on the Life Cycle Assessment (LCA) methodology, it defines the carbon footprint accounting boundary covering the "raw material acquisition - production - transportation - end-of-life" process, constructs a carbon footprint calculation model, and conducts full-life-cycle carbon footprint accounting for three typical automotive air conditioning filters. The results show that the raw material acquisition stage is the main contributor to the carbon footprint of automotive air conditioning filters, accounting for more than 60% of the total. Furthermore, approaches to reduce the product's carbon footprint are proposed, including material substitution, process optimization, energy and auxiliary material upgrading, and waste recycling. The research results provide a theoretical basis and data support for the low-carbon design, production optimization of automotive air conditioning filters, and carbon management of the automotive industry chain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".