Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration

Zhou, Xuefan; Wu, Biaoping; Liu, Yu; Zhou, Qingshi; Cheng, Wei

doi:10.1016/j.jclepro.2023.138971

Cited by 4 publications

(2 citation statements)

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“…The WHUA mainly includes nine cities, including Wuhan City and its surrounding areas, such as Huangshi, Huanggang, Xiaogan, Xianning, and Qianjiang. The total area of the WHUA is 57,800 km² (Figure 1) [47]. The region belongs to a subtropical monsoon climate, characterized by hot summers.…”

Section: Study Areamentioning

confidence: 99%

“…However, studies on the seasonal variations and driving factors of the LST in this region are still relatively scarce. Many studies have not specifically explored the effects of driving factors on various spatial units [47], but investigating their effects on spatial units would contribute to the precise implementation of urban collaborative management policies. Therefore, it is particularly important to investigate the changes in the LST and their driving factors in this region.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Xu,

Huang,

Zhang

2024

Atmosphere

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With the ongoing advancement of globalization significantly impacting the ecological environment, the continuous rise in the Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate the seasonal variations in the LST and its driving factors using mathematical models. Taking the Wuhan Urban Agglomeration (WHUA) as a case study, it explores the seasonal characteristics of the LST and employs Principal Component Analysis (PCA) to categorize the driving factors. Additionally, it compares traditional models with machine-learning models to select the optimal model for this investigation. The main conclusions are as follows. (1) The WHUA’s LST exhibits significant differences among seasons and demonstrates distinct spatial-clustering characteristics in different seasons. (2) Compared to traditional geographic spatial models, Extreme Gradient Boosting (XGBoost) shows better explanatory power in investigating the driving effects of the LST. (3) Human Activity (HA) dominates the influence throughout the year and shows a significant positive correlation with the LST; Physical Geography (PG) exhibits a negative correlation with the LST; Climate and Weather (CW) show a similar variation to the PG, peaking in the transition; and the Landscape Pattern (LP) shows a weak positive correlation with the LST, peaking in winter while being relatively inconspicuous in summer and the transition. Finally, through comparative analysis of multiple driving factors and models, this study constructs a framework for exploring the seasonal features and driving factors of the LST, aiming to provide references and guidance for the development of the WHUA and similar regions.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Xu,

Huang,

Zhang

2024

Atmosphere

View full text Add to dashboard Cite

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Comprehensive optimization of urban building cluster morphology based on microclimate: A two-level optimization approach

Liu,

She,

Wang

2024

Sustainable Cities and Society

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Predicting CO ₂ emissions through partitioned land use change simulations considering urban hierarchy

Yang,

Lu,

et al. 2024

International Journal of Digital Earth

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Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration

Cited by 4 publications

References 36 publications

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Comprehensive optimization of urban building cluster morphology based on microclimate: A two-level optimization approach

Predicting CO ₂ emissions through partitioned land use change simulations considering urban hierarchy

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Synergistic effects of heat and carbon on sustainable urban development: Case study of the Wuhan Urban Agglomeration

Cited by 4 publications

References 36 publications

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Comprehensive optimization of urban building cluster morphology based on microclimate: A two-level optimization approach

Predicting CO 2 emissions through partitioned land use change simulations considering urban hierarchy

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Predicting CO ₂ emissions through partitioned land use change simulations considering urban hierarchy