Understanding Urban Cooling of Blue–Green Infrastructure: A Review of Spatial Data and Sustainable Planning Optimization Methods for Mitigating Urban Heat Islands DOI Open Access
Grzegorz Budzik, Marta Sylla, Tomasz Kowalczyk

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 17(1), P. 142 - 142

Published: Dec. 27, 2024

Many studies in the literature have assessed blue–green infrastructure (BGI) characteristics that influence its cooling potential for sustainable urban development. Common assessment methods include satellite remote sensing, numerical simulations, and field measurements, each defining different efficiency indicators. This methodological diversity creates uncertainties optimizing BGI management. To address this, a review was conducted using Google Scholar, Web of Science, Scopus, examining how cools space, which spatial data are most effective, differences may affect results, what current research gaps innovative future directions are. The results suggest sensing is ideal large-scale comparisons, simulations local development scenarios, measurements assessing conditions closest to residents. Maximum intensity averages show 4 °C from 3 2 simulations. Differences conclusions arise resolution, model scale, delineation method, range calculation. key object size, vegetation fraction, foliage density, connectivity. Future should prioritize integration methods, shape complexity effectiveness assessment, effects morphology on evaluating characteristics’ effectiveness, explore digital twin technology management optimization. study integrates information BGI’s capabilities, serving as useful resource both practitioners researchers support resilient city

Language: Английский

Research on the cool island effect of green spaces in megacity cores: A case study of the main urban area of Xi'an, China DOI
Kaili Zhang,

Qiqi Liu,

Bin Fang

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106255 - 106255

Published: Feb. 1, 2025

Language: Английский

Citations

1

Optimizing urban park cooling effects requires balancing morphological design and landscape structure DOI Creative Commons
Lin Wang, Wenjia Wang, Fei Tang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 2, 2025

Language: Английский

Citations

0

Providing support for urban planning through investigating the cooling influence of park in Northern China: A case study of Xi'an DOI
Kai Xin, Jingyuan Zhao,

Zhaokun Li

et al.

Urban Climate, Journal Year: 2024, Volume and Issue: 58, P. 102221 - 102221

Published: Nov. 1, 2024

Language: Английский

Citations

1

Is shading a better way to cool down? Evaluation and comparison of the cooling capacity of blue-green spaces and urban shade DOI Creative Commons
Yu Zou, Jiao Chen, Hua Zong

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 167, P. 112688 - 112688

Published: Oct. 1, 2024

Language: Английский

Citations

0

Understanding Urban Cooling of Blue–Green Infrastructure: A Review of Spatial Data and Sustainable Planning Optimization Methods for Mitigating Urban Heat Islands DOI Open Access
Grzegorz Budzik, Marta Sylla, Tomasz Kowalczyk

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 17(1), P. 142 - 142

Published: Dec. 27, 2024

Many studies in the literature have assessed blue–green infrastructure (BGI) characteristics that influence its cooling potential for sustainable urban development. Common assessment methods include satellite remote sensing, numerical simulations, and field measurements, each defining different efficiency indicators. This methodological diversity creates uncertainties optimizing BGI management. To address this, a review was conducted using Google Scholar, Web of Science, Scopus, examining how cools space, which spatial data are most effective, differences may affect results, what current research gaps innovative future directions are. The results suggest sensing is ideal large-scale comparisons, simulations local development scenarios, measurements assessing conditions closest to residents. Maximum intensity averages show 4 °C from 3 2 simulations. Differences conclusions arise resolution, model scale, delineation method, range calculation. key object size, vegetation fraction, foliage density, connectivity. Future should prioritize integration methods, shape complexity effectiveness assessment, effects morphology on evaluating characteristics’ effectiveness, explore digital twin technology management optimization. study integrates information BGI’s capabilities, serving as useful resource both practitioners researchers support resilient city

Language: Английский

Citations

0