Green‐MAC‐LCCP©: Life‐cycle climate performance metric for mobile air conditioning technology choice
Bibliographic record
Abstract
Abstract Most refrigerants used today are potent chlorofluorocarbon (CFC), hydrochlorofluorocarbon (HCFC), and hydrofluorocarbon (HFC), greenhouse gases (GHG) that can contribute significantly to anthropogenic climate change and stratospheric ozone depletion. In a business‐as‐usual scenario, HFC emissions in 2050 have been predicted to be equivalent to 9–19% (CO2‐eq. basis) of projected global CO2 emissions. This percentage increases to 28–45% if projected CO2 emissions result in a 450‐ppm CO2 stabilization scenario. Half of current direct HFC emissions are from mobile air conditioning (MAC) and alternative refrigerants with lower global warming potential (GWP) should have higher energy efficiency. The European f‐Gas Directive phases out the use of refrigerants with GWP > 150, including HFC‐134a (GWP = 1430) from MACs by 2017. Life‐Cycle Climate Performance (LCCP) identifies environmentally superior technology to minimize GHG emissions from refrigeration and air conditioning applications. The comprehensive LCCP model, GREEN‐MAC‐LCCP© focuses on the current choice among alternative refrigerants that meet the f‐Gas requirement starting in 2011. Using GREEN‐MAC‐LCCP© we estimate that the current MAC technology based on HFC‐134a refrigerant demands additional fuel during vehicle A/C operation in the: USA by 7%, in the EU by 7%, in Japan by 9%, in India by 15–20%, and in China by 7–10% depending on the humidity. We compare these data with the projected LCCP CO2‐eq. savings by the year 2017 when HFC‐134a will be replaced by a low GWP alternative (GWP < 150) according to the f‐Gas rule. We find that refrigerant HFO‐1234yf has the potential to reduce global LCCP CO2‐eq. greenhouse gas emissions by about 7% in 2017, whereas greenhouse gas emissions from air conditioning systems using R‐744 (carbon dioxide) refrigerants are estimated to be about 2% greater, compared to the current HFC‐134a MAC baseline systems. © 2010 American Institute of Chemical Engineers Environ Prog, 2011
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".