Evidence map›Paper›PMID 42181256›Full record

ArticleiScience2026

Spatially heterogeneous thermal responses to urban form and greening: A multi-model analysis of seasonal and diurnal dynamics in beijing.

Hui Zheng, Yudi Li, Chen Li, Xian Su, Junling Jin

Abstract read
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hui ZhengGuangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, Guangxi Normal University, Guilin 541006, China.
Yudi LiGuangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, Guangxi Normal University, Guilin 541006, China.
Chen LiGuangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, Guangxi Normal University, Guilin 541006, China.
Xian SuGuangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, Guangxi Normal University, Guilin 541006, China.
Junling JinGuangxi Key Laboratory of Environmental Processes and Remediation in Ecologically Fragile Regions, Guangxi Normal University, Guilin 541006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban morphology and vegetation jointly regulate land surface temperature (LST) across seasonal and diurnal cycles, with implications for mitigating urban heat island (UHI) effects. Focusing on Beijing, we quantified the effects of two- and three-dimensional (2D/3D) urban form parameters and vegetation on LST using an integrated framework combining ordinary least squares (OLS), geographically weighted regression (GWR), extreme gradient boosting (XGBoost), and SHapley Additive exPlanations (SHAP). Results revealed pronounced nonlinear and spatially heterogeneous urban morphology-LST relationships and identified 250-300 m as the optimal explanatory scale. Compared with two-dimensional parameters, three-dimensional urban morphology exerted stronger control on LST, especially at night, whereas daytime cooling was dominated by the normalized difference vegetation index (NDVI). SHAP interaction analysis further showed that vegetation cooling and building-induced warming were jointly conditioned by urban form and ventilation across seasonal and diurnal contexts. These findings provide a structural basis for differentiated, scale-sensitive heat-mitigation strategies in high-density cities.

Indexed as

Applied geographyPhysical geographyUrban planning

Identifiers

PMID42181256
PMCPMC13196141

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.