TY - JOUR
T1 - Big Geodata Reveals Spatial Patterns of Built Environment Stocks Across and Within Cities in China
AU - Huang, Zhou
AU - Bao, Yi
AU - Mao, Ruichang
AU - Wang, Han
AU - Yin, Ganmin
AU - Wan, Lin
AU - Qi, Houji
AU - Li, Qiaoxuan
AU - Tang, Hongzhao
AU - Liu, Qiance
AU - Li, Linna
AU - Yu, Bailang
AU - Guo, Qinghua
AU - Liu, Yu
AU - Guo, Huadong
AU - Liu, Gang
N1 - Publisher Copyright:
© 2023 THE AUTHORS
PY - 2024/3
Y1 - 2024/3
N2 - The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important, yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources, waste, and climate strategies. However, our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited, largely owing to the lack of sufficient high spatial resolution data. This study leveraged multi-source big geodata, machine learning, and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels. The per capita built environment stock of many cities (261 tonnes per capita on average) is close to that in western cities, despite considerable disparities across cities owing to their varying socioeconomic, geomorphology, and urban form characteristics. This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades. China's urban expansion tends to be more “vertical” (with high-rise buildings) than “horizontal” (with expanded road networks). It trades skylines for space, and reflects a concentration–dispersion–concentration pathway for spatialized built environment stocks development within cities in China. These results shed light on future urbanization in developing cities, inform spatial planning, and support circular and low-carbon transitions in cities.
AB - The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important, yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources, waste, and climate strategies. However, our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited, largely owing to the lack of sufficient high spatial resolution data. This study leveraged multi-source big geodata, machine learning, and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels. The per capita built environment stock of many cities (261 tonnes per capita on average) is close to that in western cities, despite considerable disparities across cities owing to their varying socioeconomic, geomorphology, and urban form characteristics. This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades. China's urban expansion tends to be more “vertical” (with high-rise buildings) than “horizontal” (with expanded road networks). It trades skylines for space, and reflects a concentration–dispersion–concentration pathway for spatialized built environment stocks development within cities in China. These results shed light on future urbanization in developing cities, inform spatial planning, and support circular and low-carbon transitions in cities.
KW - Big geodata
KW - Built environment stock
KW - Spatial pattern
KW - Urban sustainability
KW - Urban system engineering
UR - https://www.scopus.com/pages/publications/85187975637
U2 - 10.1016/j.eng.2023.05.015
DO - 10.1016/j.eng.2023.05.015
M3 - 文章
AN - SCOPUS:85187975637
SN - 2095-8099
VL - 34
SP - 143
EP - 153
JO - Engineering
JF - Engineering
ER -