Benefits of Energy Efficient and Low-Global Warming Potential Refrigerant Cooling Equipment
Bibliographic record
Abstract
Hydrofluorocarbons (HFCs) are the fastest growing type of greenhouse gases (GHG) emissions, increasing at an annual rate of 10-15% [1]. HFCs are primarily used as refrigerants in air conditioning and refrigeration equipment and have a global warming potential (GWP) many thousands of times greater than CO2. Their rapid growth has led to a global agreement to aggressively phase down their production by amending the Montreal Protocol on Substances that Deplete the Ozone Layer [2]. We quantify the GHG benefits implementing aggressive but economic energy efficiency measures (about 30% more efficient than current technology) in air-conditioning (AC) and large commercial refrigeration equipment (CRE) together with low-GWP refrigerants. Shifting the 2030 world stock of room ACs and CRE from current levels of energy-efficiency and high-GWP refrigerants to “economic” energy efficiency levels and low-GWP refrigerants by 2050 would avoid up to 240.1 GT CO2e and shifting to “best-available technology” energy efficiency levels and low GWP refrigerants by 2050 would avoid up to 373 GT CO2e with existing electricity grid emission factors. About two-thirds of this cumulative savings are from reduced electricity sector emissions from improved energy efficiency. Thus, it is highly beneficial to pursue high energy efficiency in concert with the transition to lower GWP refrigerants to achieve maximal GHG reductions with the least amount of equipment re-design and replacement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".