Bibliographic record
Abstract
The Intergovernmental Panel on Climate Change (IPCC) report states that buildings account 32 percent of the energy use and 19 per cent of the global emissions out of which almost a third can be attributed to the cooling sector. The aim of this paper is to establish the energy efficiency benefits and discuss health benefits from energy efficiency improvements in the emerging economies such as India. Air conditioners are important in targeting the improvement of energy efficiency policies and are the low-hanging fruit of climate policy in India because of its size and rapidly increasing market due to the aspirations of people in the country to improve their standard of living. Montreal Protocol’s latest Amendment, that limits the use of a supergreenhouse gas Hydrofluorocarbons (HFCs) will provide an impetus to the energy efficiency innovations, and has historically catalyzed energy efficiency improvements of up to 35 per cent. Projected growth in power demand for cooling could have a crippling effect on India’s fiscal status and prospects for economic growth to the extent it becomes necessary to further increase fossil fuel imports to maintain pace with high demand for electricity in the cooling sector. Modest energy efficiency improvements saves India billions of US dollars annually, several scores of Gigawatts (GW) of electricity, improved health outcomes in terms of reduced mortality and respiratory diseases and also indirect social impacts from improved health. Thus this paper sheds light on these benefits and establishing a clear unmistakable link between these energy efficiency improvements and health.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".