Quantifying spatiotemporal dynamics of urban building and material metabolism by combining a random forest model and GIS-based material flow analysis

被引:9
|
作者
Mao, Ting [1 ,2 ,3 ]
Liu, Yupeng [1 ,2 ,3 ]
Chen, Wei-Qiang [1 ,2 ,3 ]
Li, Nan [1 ,2 ,3 ]
Dong, Nan [4 ]
Shi, Yao [5 ]
机构
[1] Chinese Acad Sci, Inst Urban Environm, Key Lab Urban Environm & Hlth, Xiamen, Peoples R China
[2] Xiamen Key Lab Urban Metab, Xiamen, Peoples R China
[3] Univ Chinese Acad Sci, Beijing, Peoples R China
[4] Beijing CityDNA Technol Co, East Ring 3, Beijing, Peoples R China
[5] Chinese Acad Sci, Inst Proc Engn, Natl Engn Res Ctr Green Recycling Strateg Met Res, CAS Key Lab Green Proc & Engn, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
material flow analysis; geographic information systems; spatiotemporal analysis; random forest; building vintage; industrial ecology; high-resolution urban grids; MATERIAL STOCK ANALYSIS; CONSTRUCTION; REGRESSION; WASTE; TIME; AGE;
D O I
10.3389/feart.2022.944865
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Understanding building metabolism is critical for guiding urban resource management and addressing challenges in urban sustainable development. Key attributes of buildings, including geolocation, footprint, height, and vintage, are crucial to characterizing spatiotemporal patterns of building metabolism. However, these attributes are usually challenging to obtain broadly and automatically, which obscures a comprehensive understanding and accurate assessment of urban metabolism. Moreover, the lack of a finer spatial pattern of these attributes shadows a spatially explicit characterization of material stock and flow in cities. In this study, we took Shenzhen-whose urbanization over the past three decades has been unprecedented in China and even around the world- has been taken as an example to develop a city-level building dataset based on a random-forest model and quantify the spatiotemporal patterns of material metabolism at relatively high spatial resolution (in 500 m x 500 m grids) by combing material flow analysis (MFA) with geographic information system (GIS). The results show that Shenzhen grew from a small town with 281.02 x 10(6) m(3) of buildings in the 1990s to a mega-city with 3585.5 x 10(6) m(3) of buildings in 2018 and expanded both outward and upward from downtown to suburban areas. The urban "weight " (material stock) increased from 92.69 Mt in the 1990s to 1667.8 Mt in 2018 and tended to be saturated, with an average growth rate of 9.5% per year. Spatially, the south-central areas were the largest container of material stocks and generated the most demolition waste. The spatially explicit maps of building three-dimensional (3-D) form and vintage provide detailed information for architectural conservation and could support the decision-making for urban renewal planning. The spatiotemporal patterns of in-use material stocks and potential generation of construction and demolition waste (CDW) provide a benchmark of environmental risk assessment and potential secondary resources to reduce "original " material consumption, which could help alter urban renewal to an environmental-friendly and sustainable trajectory.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Applying material and energy flow analysis to assess urban metabolism in the context of the circular economy
    Papageorgiou, Asterios
    Bjorklund, Anna
    Sinha, Rajib
    JOURNAL OF INDUSTRIAL ECOLOGY, 2024, 28 (04) : 885 - 900
  • [22] Combining material flow analysis with life cycle assessment to identify environmental hotspots of urban consumption
    Westin, Alexandra Lavers
    Kalmykova, Yuliya
    Rosado, Leonardo
    Oliveira, Felipe
    Laurenti, Rafael
    Rydberg, Tomas
    JOURNAL OF CLEANER PRODUCTION, 2019, 226 : 526 - 539
  • [23] Material Flow Analysis and Carbon Footprint of Forest Resources in Japan: A Case Study on Building Materials
    Wang, Hongjun
    Takano, Atsushi
    SSRN, 2022,
  • [24] Dispersal and habitat connectivity in complex heterogeneous landscapes: An analysis with a GIS-based random walk model
    Schippers, P
    Verboom, J
    Knaapen, JP
    vanApeldoorn, RC
    ECOGRAPHY, 1996, 19 (02) : 97 - 106
  • [25] A Probabilistic Dynamic Material Flow Analysis Model for Chinese Urban Housing Stock
    Cao, Zhi
    Shen, Lei
    Zhong, Shuai
    Liu, Litao
    Kong, Hanxiao
    Sun, Yanzhi
    JOURNAL OF INDUSTRIAL ECOLOGY, 2018, 22 (02) : 377 - 391
  • [26] Remote sensing and GIS-based analysis of urban dynamics and modelling of its drivers, the case of Pingtan, China
    Eshetu Shifaw
    Jinming Sha
    Xiaomei Li
    Shang Jiali
    Zhongcong Bao
    Environment, Development and Sustainability, 2020, 22 : 2159 - 2186
  • [27] Remote sensing and GIS-based analysis of urban dynamics and modelling of its drivers, the case of Pingtan, China
    Shifaw, Eshetu
    Sha, Jinming
    Li, Xiaomei
    Jiali, Shang
    Bao, Zhongcong
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2020, 22 (03) : 2159 - 2186
  • [28] Spatial mapping of artesian zone at Iraqi southern desert using a GIS-based random forest machine learning model
    Al-Abadi A.M.
    Shahid S.
    Modeling Earth Systems and Environment, 2016, 2 (2)
  • [29] A review of a series of effective methods in urban metabolism: Material flow, ecological network and factor analysis
    Wang, Xinjing
    Tan, Xuan
    Gao, Minxuan
    Zhang, Yan
    SUSTAINABLE PRODUCTION AND CONSUMPTION, 2023, 39 : 162 - 174
  • [30] Quantification of household electricity consumption for supporting energy efficiency of urban metabolism: Material flow analysis
    Ali, Sharif Shofirun Sharif
    Kasavan, Saraswathy
    Razman, Muhammad Rizal
    Awang, Azahan
    Zarco-Perinan, Pedro J.
    INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED, 2024, 13 (05): : 960 - 973