Matching supply and demand of cooling service provided by urban green and blue space

Urban forestry & urban greening, Journal Year: 2024, Volume and Issue: 96, P. 128338 - 128338

Published: April 25, 2024

Language: Английский

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Spatiotemporal effects of lake-land breezes on the microclimate of lakefront trees

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 119, P. 106127 - 106127

Published: Jan. 5, 2025

Language: Английский

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Parks may not be effective enough to improve the thermal environment in Shanghai (China) as our modified H3SFCA method suggests

Building and Environment, Journal Year: 2024, Volume and Issue: 253, P. 111291 - 111291

Published: Feb. 15, 2024

Language: Английский

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Exploring the impact of urban morphology on river cooling effects: A case study of the Arakawa river in Tokyo

Ecological Indicators, Journal Year: 2025, Volume and Issue: 172, P. 113288 - 113288

Published: Feb. 28, 2025

Language: Английский

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Impact of diurnal variation in 3D urban landscape metrics on land surface temperature in Shanghai: A local climate zone perspective

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115624 - 115624

Published: March 1, 2025

Language: Английский

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Modelling and optimization of urban green-blue infrastructure design for city cooling

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 113096 - 113096

Published: April 1, 2025

Language: Английский

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Impact of blue spaces on urban microclimate in different climate zones, daytimes and seasons – a systematic review

Urban forestry & urban greening, Journal Year: 2024, Volume and Issue: unknown, P. 128528 - 128528

Published: Oct. 1, 2024

Language: Английский

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Diurnal Variation Reveals the Characteristics and Influencing Factors of Cool Island Effects in Urban Blue-Green Spaces

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2115 - 2115

Published: Nov. 29, 2024

Urban blue-green space cooling island effect (BGCI) is effective in improving the thermal comfort of residents. However, there little knowledge regarding diurnal variation BGCIs and influencing factors. Therefore, we selected Beijing as study area used ECOSTRESS LST data inflection–maximum perspective method to explore BGCIs. Additionally, investigated variations relative influence characteristics itself, well surrounding 2D 3D landscape metrics using boosted regression tree model. The results indicated that displayed distinct patterns. progressively increased from sunrise midday, decreased thereafter sunset, reached its peak around diminished a relatively low level constant intensity at night. water bodies exhibited significantly higher compared vegetation during day, particularly with difference mean (CI) 1.06 °C distance (CD) 63.27 m, while differences were minimal night CI 0.02 CD 9.64 m. features had more significant impact on midday (CI: 32.30% 13.86% night), influenced 26.40% 35.81% night). greater (52.87% 10.46% playing important role (15.56% 38.28% height, shape index vegetation, building coverage ratio opposite trends Optimizing spaces cost-effective than themselves for nighttime comfort, especially urban landscapes. These findings emphasize imperative essentiality exploring BGCIs, providing valuable information mitigating UHI effects.

Language: Английский

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Evaluating the natural cooling potential of waterbodies in dense urban landscape: A case study of Bengaluru, India

Urban Climate, Journal Year: 2024, Volume and Issue: 58, P. 102200 - 102200

Published: Nov. 1, 2024

Language: Английский

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Quantitatively comparing the morphological influences on the cool island effect in urban waterfront blue-green spaces across six cities near 30°N

Urban Climate, Journal Year: 2024, Volume and Issue: 56, P. 102076 - 102076

Published: July 1, 2024

Language: Английский

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