Product, building, and infrastructure material stocks dataset for 337 Chinese cities between 1978 and 2020

Reliable city-level product, building, and infrastructure material stocks data are essential for understanding historical material use patterns, benchmarking material efficiency, and informing future recycling potentials. However, such urban material stocks data are often limited, due primarily to u...

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Bibliographic Details
Published in:Scientific data 2023-04, Vol.10 (1), p.228-228, Article 228
Main Authors: Li, Xiang, Song, Lulu, Liu, Qiance, Ouyang, Xin, Mao, Ting, Lu, Haojie, Liu, Litao, Liu, Xiaojie, Chen, Weiqiang, Liu, Gang
Format: Article
Language:English
Subjects:
704/844/2787
704/844/685
Climate change
Data Descriptor
Datasets
Humanities and Social Sciences
Industrial ecology
Infrastructure
multidisciplinary
Science
Science (multidisciplinary)
Statistics
Urbanization
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Summary:Reliable city-level product, building, and infrastructure material stocks data are essential for understanding historical material use patterns, benchmarking material efficiency, and informing future recycling potentials. However, such urban material stocks data are often limited, due primarily to unavailable, inconsistent, or noncontinuous city-level statistics. Here, we provided such an Urban Product, Building, and Infrastructure Material Stocks (UPBIMS) dataset for China, a country that has undergone a remarkable urbanization process in the past decades, by collating different official statistics and applying various gap-filling methods. This dataset contains the stock of 24 materials contained in 10 types of products, buildings, and infrastructure in all 337 prefecture-level cities in China from 1978 to 2020. This quality controlled and unified dataset is the first of its kind with such a full coverage of all prefecture-level Chinese cities and can be used in a variety of applications, for example in urban geography, industrial ecology, circular economy, and climate change mitigation. Every piece of data is tagged with its source and the dataset will be periodically updated.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-023-02143-w