Duo Cui, Siyao Yang, Xuanren Song, Xiaoting Huang, Cuncun Duan, Mingrui Ji, Zhongyan Li, Siqi Yu, Zhu Deng, Piyu Ke, Xinyu Dou, Taochun Sun, Zhu Liu
{"title":"Monthly methane emissions in Chinese mainland provinces from 2013-2022.","authors":"Duo Cui, Siyao Yang, Xuanren Song, Xiaoting Huang, Cuncun Duan, Mingrui Ji, Zhongyan Li, Siqi Yu, Zhu Deng, Piyu Ke, Xinyu Dou, Taochun Sun, Zhu Liu","doi":"10.1038/s41597-025-05107-4","DOIUrl":null,"url":null,"abstract":"<p><p>As the world's largest source of methane emissions, accurately measuring and tracking China's emissions across various sectors is essential for global climate change efforts. Methane, a potent greenhouse gas, is emitted from diverse anthropogenic and natural sources, many of which exhibit pronounced temporal variability. In particular, emissions from rice cultivation, energy use, and livestock management show strong seasonal patterns, yet high-frequency and spatially detailed methane emission inventories have been lacking. This study introduces the Monthly Methane Emission Inventory for China's Provinces (MMCP), a comprehensive dataset covering the period from January 2013 to December 2022. The dataset includes emissions data from eight key sectors: coal mining, oil and gas systems, energy combustion, rice cultivation, livestock, solid waste, wastewater, and wetlands. By offering sector-specific and temporally resolved emission estimates, MMCP serves as a valuable resource for scientific research, policy evaluation, and emission mitigation planning. This inventory facilitates improved understanding of emission trends and supports more accurate modeling of atmospheric methane concentrations and climate feedbacks.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"948"},"PeriodicalIF":6.9000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141455/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05107-4","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}
引用次数: 0
Abstract
As the world's largest source of methane emissions, accurately measuring and tracking China's emissions across various sectors is essential for global climate change efforts. Methane, a potent greenhouse gas, is emitted from diverse anthropogenic and natural sources, many of which exhibit pronounced temporal variability. In particular, emissions from rice cultivation, energy use, and livestock management show strong seasonal patterns, yet high-frequency and spatially detailed methane emission inventories have been lacking. This study introduces the Monthly Methane Emission Inventory for China's Provinces (MMCP), a comprehensive dataset covering the period from January 2013 to December 2022. The dataset includes emissions data from eight key sectors: coal mining, oil and gas systems, energy combustion, rice cultivation, livestock, solid waste, wastewater, and wetlands. By offering sector-specific and temporally resolved emission estimates, MMCP serves as a valuable resource for scientific research, policy evaluation, and emission mitigation planning. This inventory facilitates improved understanding of emission trends and supports more accurate modeling of atmospheric methane concentrations and climate feedbacks.
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.