Dissecting the nonlinear economic implications of urban extreme thermo-environment using a Monte Carlo simulation-based ensemble learning model DOI
Xiaochang Liu, Renlu Qiao,

Zhang Xiuning

и другие.

Habitat International, Год журнала: 2024, Номер 156, С. 103274 - 103274

Опубликована: Дек. 24, 2024

Язык: Английский

Impacts of Land Use Characteristics on Extreme Heat Events: Insights from Explainable Machine Learning Model DOI
Hangying Su, Zhuoxu Qi,

Q. Wang

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106139 - 106139

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

4
The nonlinear climatological impacts of urban morphology on extreme heats in urban functional zones: An interpretable ensemble learning-based approach DOI
Xiaochang Liu, Tao Wu, Qingrui Jiang

и другие.

Building and Environment, Год журнала: 2025, Номер 273, С. 112728 - 112728

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

0
Assessment of Low-Carbon Utilization in Territorial Space and Identification of Its Driving Factors: A Case Study of the Yangtze River Economic Belt in China DOI Creative Commons

Liangzhao Chen,

Peng Tang, Jinhua Li

и другие.

Land, Год журнала: 2025, Номер 14(4), С. 738 - 738

Опубликована: Март 29, 2025

The low-carbon utilization (LCU) of territorial space represents a critical strategy for addressing climate change and promoting sustainable development, yet current assessments in this domain remain insufficient. This study develops an integrated assessment framework utilizing kernel density estimation, optimal parameter-based geographical detector, the Tobit regression model to analyze spatiotemporal evolution, typology, driving factors LCU Yangtze River Economic Belt. findings reveal that index region increased from 0.548 2005 0.569 2020, despite significant regional disparities. Cities are classified into eight distinct types LCU, with over 80% demonstrating poor performance at least one functional space, particularly urban where number cities below average is highest. analysis demonstrates spaces influenced by integration natural conditions, socio-economic factors, landscape patterns. In light these findings, systematically proposes policy recommendations enhance space. research contributes establishment scientific evaluation providing empirical evidence improve spatial governance policies support development.

Язык: Английский

Процитировано

0
Exploring the spatial effects of rapid urbanization on land use efficiency in China under Low-Carbon constraints DOI

Chuanjian Yi,

Bo Xu,

Xiaoyan Shi

и другие.

Ecological Indicators, Год журнала: 2025, Номер 174, С. 113442 - 113442

Опубликована: Апрель 12, 2025

Язык: Английский

Процитировано

0
Dissecting the nonlinear economic implications of urban extreme thermo-environment using a Monte Carlo simulation-based ensemble learning model DOI
Xiaochang Liu, Renlu Qiao,

Zhang Xiuning

и другие.

Habitat International, Год журнала: 2024, Номер 156, С. 103274 - 103274

Опубликована: Дек. 24, 2024

Язык: Английский

Процитировано

2