City-level process-related CO2 emissions in China 2000–2021

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.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-08-15
DOI
https://doi.org/10.1038/s41597-025-05782-3
Citations
4
Primary Topic
Environmental Impact and Sustainability
Type
article
Field-Weighted Citation Impact
3.20

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

City-level process-related CO2 emissions in China 2000–2021

Yuru Guan, Shijie Cai, Jun Bi, Yuli Shan et al.
4 citations
Scientific Data
Environmental Impact and Sustainability
3.20
article

City-level process-related CO2 emissions in China 2000–2021

Yuru Guan, Shijie Cai, Jun Bi, Yuli Shan, Jinghang Xu, Chang Tan, Menghan Liu
article en
4 citations

Abstract

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.

Scientific DataVol. 12(1)
State Key Laboratory of Pollution Control and Resource Reuse (CN), National Center for Climate Change Strategy and International Cooperation (CN), University of Birmingham (GB), Nanjing University (CN), Tsinghua University (CN)
UK Research and Innovation, National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, National Key Research and Development Program of China
Climate action
Openalex Percentile: Top 8%
Environmental Impact and Sustainability
3.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.