{"title":"2000 至 2020 年中国 281 个城市的城市级能量代谢账户。","authors":"Miaohan Tang, Gengyuan Liu, Jingke Hong, Weier Liu, Chao Zhong, Dan Wang, Yuli Shan","doi":"10.1038/s41597-024-04344-3","DOIUrl":null,"url":null,"abstract":"<p><p>Cities exhibit diverse urban metabolism patterns in terms of the natural environment, industrial composition, energy, and material consumption. A chronicled city-level quantification of emergy metabolic flows over time can significantly enhance the understanding of the temporal dynamics and urban metabolism patterns, which provides critical insights for the transitions to sustainability. However, there exists no city-level urban emergy metabolism dataset in China that can support detailed spatial-temporal analysis. In this study, we present a city-level urban emergy metabolism dataset of China's 281 cities between 2000 and 2020. This dataset meticulously describes the production and import of 41 resource types that sustain metabolic activities in 281 Chinese cities over a span of 21 years. This database, for the first time, provides insights into the historical metabolic changes of China's cities by detailing emergy flows including production, consumption, and import of resources. Furthermore, these emergy flow inventories serve as valuable resources for studying urban metabolic characteristics, evaluating policy impacts, and formulating sustainable development strategies for China's cities.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"55"},"PeriodicalIF":6.9000,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724914/pdf/","citationCount":"0","resultStr":"{\"title\":\"City-level emergy metabolism accounts for China's 281 cities from 2000 to 2020.\",\"authors\":\"Miaohan Tang, Gengyuan Liu, Jingke Hong, Weier Liu, Chao Zhong, Dan Wang, Yuli Shan\",\"doi\":\"10.1038/s41597-024-04344-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Cities exhibit diverse urban metabolism patterns in terms of the natural environment, industrial composition, energy, and material consumption. A chronicled city-level quantification of emergy metabolic flows over time can significantly enhance the understanding of the temporal dynamics and urban metabolism patterns, which provides critical insights for the transitions to sustainability. However, there exists no city-level urban emergy metabolism dataset in China that can support detailed spatial-temporal analysis. In this study, we present a city-level urban emergy metabolism dataset of China's 281 cities between 2000 and 2020. This dataset meticulously describes the production and import of 41 resource types that sustain metabolic activities in 281 Chinese cities over a span of 21 years. This database, for the first time, provides insights into the historical metabolic changes of China's cities by detailing emergy flows including production, consumption, and import of resources. Furthermore, these emergy flow inventories serve as valuable resources for studying urban metabolic characteristics, evaluating policy impacts, and formulating sustainable development strategies for China's cities.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"55\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-01-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11724914/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-024-04344-3\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04344-3","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
City-level emergy metabolism accounts for China's 281 cities from 2000 to 2020.
Cities exhibit diverse urban metabolism patterns in terms of the natural environment, industrial composition, energy, and material consumption. A chronicled city-level quantification of emergy metabolic flows over time can significantly enhance the understanding of the temporal dynamics and urban metabolism patterns, which provides critical insights for the transitions to sustainability. However, there exists no city-level urban emergy metabolism dataset in China that can support detailed spatial-temporal analysis. In this study, we present a city-level urban emergy metabolism dataset of China's 281 cities between 2000 and 2020. This dataset meticulously describes the production and import of 41 resource types that sustain metabolic activities in 281 Chinese cities over a span of 21 years. This database, for the first time, provides insights into the historical metabolic changes of China's cities by detailing emergy flows including production, consumption, and import of resources. Furthermore, these emergy flow inventories serve as valuable resources for studying urban metabolic characteristics, evaluating policy impacts, and formulating sustainable development strategies for China's cities.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.