Climate considerations for R/AC equipment operation: is the answer in energy efficiency ?
Bibliographic record
Abstract
Since a long time, energy efficiency has been an important issue in R/AC equipment development; currently its relation to climate is considered most important. Energy efficient use of ammonia or hydrocarbons in equipment relates to a number of aspects already realised; this also applies to CO2, which is under further development. In the case of fluorocarbons, it is different since direct emissions (i.e., from high GWP HFCs) are typically to be considered in light of the Kigali Amendment. However, even with the “Kigali” HFC phase-down, addressing of energy efficiency related indirect GHG emissions often receives a lot of emphasis in that context. “Kigali” is strictly related to regulatory measures to phase down and to compliance; “Kigali” and efficiency are two different things. If one aims at a substantial reduction of all climate relevant emissions for all applications, efforts need to be undertaken from a broader perspective. Priorities are on energy efficiency, but even more on initiatives focused to achieve capacity reductions within comprehensive frameworks. Lowering demand via influencing outside parameters, guaranteeing adequate, high-efficient equipment operation, shall be considered priority number one, and can be defined as using energy efficiently. Even though the total capacity of large units is lower than the capacity in other sectors, food production, cold storage and chillers can contribute to savings via efficient energy use. While the efforts under the Montreal Protocol are important, a different or perhaps enhanced framework will be required to effectively address a more efficient use of energy in R/AC equipment, aiming at the highly emphasised ‘low carbon profile’ for the R/AC sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".