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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119088/ https://www.ncbi.nlm.nih.gov/pubmed/37080990 http://dx.doi.org/10.1038/s41597-023-02143-w |
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author | Li, Xiang Song, Lulu Liu, Qiance Ouyang, Xin Mao, Ting Lu, Haojie Liu, Litao Liu, Xiaojie Chen, Weiqiang Liu, Gang |
author_facet | Li, Xiang Song, Lulu Liu, Qiance Ouyang, Xin Mao, Ting Lu, Haojie Liu, Litao Liu, Xiaojie Chen, Weiqiang Liu, Gang |
author_sort | Li, Xiang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10119088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101190882023-04-22 Product, building, and infrastructure material stocks dataset for 337 Chinese cities between 1978 and 2020 Li, Xiang Song, Lulu Liu, Qiance Ouyang, Xin Mao, Ting Lu, Haojie Liu, Litao Liu, Xiaojie Chen, Weiqiang Liu, Gang Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-04-20 /pmc/articles/PMC10119088/ /pubmed/37080990 http://dx.doi.org/10.1038/s41597-023-02143-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Li, Xiang Song, Lulu Liu, Qiance Ouyang, Xin Mao, Ting Lu, Haojie Liu, Litao Liu, Xiaojie Chen, Weiqiang Liu, Gang Product, building, and infrastructure material stocks dataset for 337 Chinese cities between 1978 and 2020 |
title | Product, building, and infrastructure material stocks dataset for 337 Chinese cities between 1978 and 2020 |
title_full | Product, building, and infrastructure material stocks dataset for 337 Chinese cities between 1978 and 2020 |
title_fullStr | Product, building, and infrastructure material stocks dataset for 337 Chinese cities between 1978 and 2020 |
title_full_unstemmed | Product, building, and infrastructure material stocks dataset for 337 Chinese cities between 1978 and 2020 |
title_short | Product, building, and infrastructure material stocks dataset for 337 Chinese cities between 1978 and 2020 |
title_sort | product, building, and infrastructure material stocks dataset for 337 chinese cities between 1978 and 2020 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119088/ https://www.ncbi.nlm.nih.gov/pubmed/37080990 http://dx.doi.org/10.1038/s41597-023-02143-w |
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