Future-proofing sustainable cooling demand
Bibliographic record
Abstract
The global cooling demand is expected to grow substantially during the 21st century. Apart from the increasing temperatures due to climate change, this demand will also be driven by a set of demographic characteristics, including population growth, urbanization, increasing incomes, social policies and commitments, and improved access to electricity. Indeed, space cooling demand could increase by 300% globally by 2050 and is concentrated in the hotter regions of the world with growing populations and incomes. However, this demand will likely contribute to its own growth if delivered along conventional patterns, significantly increasing GHG emissions due to high energy consumption as well as leakage of refrigerants, and hence compromising many of our economic, environmental, social, and political goals, targets, and commitments. We present a system-level approach to cooling provision in buildings and urban environments, while also highlighting the need for a holistic consideration of the cooling demand across other sectors (e.g., transport), to ensure sustainability and resilience throughout the life cycle of buildings and wider infrastructure. We aim to drive a new system level thinking in key areas – how we mitigate, make, store, move, manage, finance, and regulate cold – to meet current and future cooling needs efficiently, sustainably, and affordably, while building resilience in line with the ambitions of the Paris Agreement, the Kigali Amendment to the Montreal Protocol, and the UN Sustainable Development Goals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".