City-level process-related CO2 emissions in China 2000–2021
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05782-3 |
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| author | Sijia Cai Jinghang Xu Yuru Guan Miaomaio Liu Chang Tan Jun Bi Yuli Shan |
| author_facet | Sijia Cai Jinghang Xu Yuru Guan Miaomaio Liu Chang Tan Jun Bi Yuli Shan |
| author_sort | Sijia Cai |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-ee6fdfffb87547a0bd7d76046c5f487b |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-ee6fdfffb87547a0bd7d76046c5f487b2025-08-20T04:01:43ZengNature PortfolioScientific Data2052-44632025-08-011211910.1038/s41597-025-05782-3City-level process-related CO2 emissions in China 2000–2021Sijia Cai0Jinghang Xu1Yuru Guan2Miaomaio Liu3Chang Tan4Jun Bi5Yuli Shan6State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversitySchool of Geography, Earth and Environmental Sciences, University of BirminghamSchool of Geography, Earth and Environmental Sciences, University of BirminghamState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversityDepartment of Earth System Science, Tsinghua UniversityState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversitySchool of Geography, Earth and Environmental Sciences, University of BirminghamAbstract 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.https://doi.org/10.1038/s41597-025-05782-3 |
| spellingShingle | Sijia Cai Jinghang Xu Yuru Guan Miaomaio Liu Chang Tan Jun Bi Yuli Shan City-level process-related CO2 emissions in China 2000–2021 Scientific Data |
| title | City-level process-related CO2 emissions in China 2000–2021 |
| title_full | City-level process-related CO2 emissions in China 2000–2021 |
| title_fullStr | City-level process-related CO2 emissions in China 2000–2021 |
| title_full_unstemmed | City-level process-related CO2 emissions in China 2000–2021 |
| title_short | City-level process-related CO2 emissions in China 2000–2021 |
| title_sort | city level process related co2 emissions in china 2000 2021 |
| url | https://doi.org/10.1038/s41597-025-05782-3 |
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