ArticleNature communications2026
Efficiency-weighted cooling degree days reveal opposing temperature and humidity effects on energy demand.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Efficiency-weighted cooling degree days reveal opposing temperature and humidity effects on energy demand.Nature communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Cooling degree days (CDD) are widely used to estimate air-conditioning energy demand, yet they implicitly assume constant refrigeration efficiency, neglecting its dependence on temperature and humidity. We introduce an efficiency-aware cooling metric -effective cooling degree days (eCDD)- that links ambient temperature and humidity to the physical work required for cooling. Here we show that, across North America, cooling efficiency has declined by 2-4% per decade since 1971, and that regionally opposing trends in temperature and humidity cause CDD to misrepresent cooling demand. During hot extremes, efficiency losses are amplified under humid-heat but partially offset under dry-heat. Projections reveal a continent-scale shift in humidity regimes, with an eastward extension of dry heat that enhances cooling efficiency during extremes, while eCDD increases by 10-80%. These results demonstrate that temperature-based metrics alone are insufficient and efficiency-aware metrics are essential for accurately assessing cooling demand, especially considering differing electricity generation mixes.
Identifiers
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Registered trials
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