{"title":"2000-2021年中国城市过程相关CO2排放量","authors":"Sijia Cai, Jinghang Xu, Yuru Guan, Miaomaio Liu, Chang Tan, Jun Bi, Yuli Shan","doi":"10.1038/s41597-025-05782-3","DOIUrl":null,"url":null,"abstract":"<p><p>As the world's largest CO<sub>2</sub> emitter, China needs accurate city-level CO<sub>2</sub> emission accounts to formulate effective low-carbon policies. However, previous studies mainly accounted for emissions from fossil fuel combustion and overlooked process-related CO<sub>2</sub> emissions from industrial production (e.g., mineral, chemical, metal products), which account for approximately 13% of China's total emissions. In this study, we built the first time-series dataset of process-related CO<sub>2</sub> emissions for 289 Chinese cities from 2000 to 2021. The dataset covers 11 industrial products and adheres to the methodology recommended by the Intergovernmental Panel on Climate Change (IPCC). We applied China-specific emission factors and compiled industrial output data from city statistical yearbooks and bulletins. Missing output data were imputed using missForest models. The estimated uncertainty of the process-related emissions in our dataset ranges from 3.87% to 3.91%. Our dataset provides a robust foundation for analyzing emission patterns at the city level and for designing targeted low-carbon policies.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1435"},"PeriodicalIF":6.9000,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356871/pdf/","citationCount":"0","resultStr":"{\"title\":\"City-level process-related CO<sub>2</sub> emissions in China 2000-2021.\",\"authors\":\"Sijia Cai, Jinghang Xu, Yuru Guan, Miaomaio Liu, Chang Tan, Jun Bi, Yuli Shan\",\"doi\":\"10.1038/s41597-025-05782-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>As the world's largest CO<sub>2</sub> emitter, China needs accurate city-level CO<sub>2</sub> emission accounts to formulate effective low-carbon policies. However, previous studies mainly accounted for emissions from fossil fuel combustion and overlooked process-related CO<sub>2</sub> emissions from industrial production (e.g., mineral, chemical, metal products), which account for approximately 13% of China's total emissions. In this study, we built the first time-series dataset of process-related CO<sub>2</sub> emissions for 289 Chinese cities from 2000 to 2021. The dataset covers 11 industrial products and adheres to the methodology recommended by the Intergovernmental Panel on Climate Change (IPCC). We applied China-specific emission factors and compiled industrial output data from city statistical yearbooks and bulletins. Missing output data were imputed using missForest models. The estimated uncertainty of the process-related emissions in our dataset ranges from 3.87% to 3.91%. Our dataset provides a robust foundation for analyzing emission patterns at the city level and for designing targeted low-carbon policies.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"1435\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356871/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-05782-3\",\"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/s41597-025-05782-3","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 process-related CO2 emissions in China 2000-2021.
As the world's largest CO2 emitter, China needs accurate city-level CO2 emission accounts to formulate effective low-carbon policies. However, previous studies mainly accounted for emissions from fossil fuel combustion and overlooked process-related CO2 emissions from industrial production (e.g., mineral, chemical, metal products), which account for approximately 13% of China's total emissions. In this study, we built the first time-series dataset of process-related CO2 emissions for 289 Chinese cities from 2000 to 2021. The dataset covers 11 industrial products and adheres to the methodology recommended by the Intergovernmental Panel on Climate Change (IPCC). We applied China-specific emission factors and compiled industrial output data from city statistical yearbooks and bulletins. Missing output data were imputed using missForest models. The estimated uncertainty of the process-related emissions in our dataset ranges from 3.87% to 3.91%. Our dataset provides a robust foundation for analyzing emission patterns at the city level and for designing targeted low-carbon policies.
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.