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Record W2787076884 · doi:10.5194/acp-2016-1046

A wedge strategy for mitigation of urban warming in future climate scenarios

2017· article· en· W2787076884 on OpenAlexaboutno aff
Lei Zhao, Xuhui Lee, Natalie M. Schultz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsnot available
FundersYale UniversityMinistry of Education of the People's Republic of ChinaNational Science Foundation
KeywordsUrban heat islandEnvironmental scienceVegetation (pathology)Greenhouse gasClimate changeGlobal warmingPopulationUrban climateUrban planningClimatologyPhysical geographyGeographyMeteorologyCivil engineeringGeology

Abstract

fetched live from OpenAlex

Abstract. Heat stress is one of the most severe climate threats to the human society in a future warmer world. The situation is further compounded in urban areas by the urban heat island (UHI). Because the majority of world's population is projected to live in cities, there is a pressing need to find effective solutions for the high temperature problem. We use a climate model to investigate the effectiveness of urban heat mitigation strategies: cool roofs, street vegetation, green roofs, and reflective pavement. Our results show that by adopting highly-reflective roofs, almost all the cities in the United States and southern Canada are transformed into white oases at midday, with an average oasis effect of −3.4 K in the summer for the period 2071–2100, which offsets approximately 80 % of the greenhouse gas (GHG) warming projected for the same period under the RCP4.5 scenario. A UHI mitigation wedge consisting of cool roofs, street vegetation and reflective pavement has the potential to eliminate the daytime UHI plus the GHG warming.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.261
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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