High temporal and spatial resolution projected electricity carbon emission factors of China from 2025–2060

China's electric power sector plays a pivotal role in the country's transition to carbon neutrality. Accurate projections of electricity carbon emission factors with high temporal and spatial resolution are critical for assessing the decarbonization process of not only the power sector but also other sectors such as transportation, building, and industrial manufacturing. Existing datasets primarily focus on historical averages and lack high-resolution forecasts for future emissions. This study presents a comprehensive dataset of projected hourly electricity carbon emission factors for China from 2025 to 2060, offering a temporal resolution of one hour and covering 31 provinces. The dataset, consisting of annual 8760 hourly carbon emission factors for five power system development scenarios, is generated using a simulation method that combines power system planning and operational models. These models account for the future development of the power system, including generation-grid-load-storage co-optimization, and carbon emission flow calculations to derive high-resolution factors. The methodology's accuracy is validated by comparing simulated emission factors with official data, yielding Mean Absolute Percentage Errors (MAPEs) below 2.525%.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2026-04-21
DOI
https://doi.org/10.1038/s41597-026-07272-6
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

High temporal and spatial resolution projected electricity carbon emission factors of China from 2025–2060

Shixu Zhang, Yaowang Li, Hongyi Wei, Weiwei Li et al.
Scientific Data
Integrated Energy Systems Optimization
article

High temporal and spatial resolution projected electricity carbon emission factors of China from 2025–2060

Shixu Zhang, Yaowang Li, Hongyi Wei, Weiwei Li, Yuting Qin, Yuliang Liu, Ershun Du, Chongqing Kang, Ning Zhang
article en

Abstract

China's electric power sector plays a pivotal role in the country's transition to carbon neutrality. Accurate projections of electricity carbon emission factors with high temporal and spatial resolution are critical for assessing the decarbonization process of not only the power sector but also other sectors such as transportation, building, and industrial manufacturing. Existing datasets primarily focus on historical averages and lack high-resolution forecasts for future emissions. This study presents a comprehensive dataset of projected hourly electricity carbon emission factors for China from 2025 to 2060, offering a temporal resolution of one hour and covering 31 provinces. The dataset, consisting of annual 8760 hourly carbon emission factors for five power system development scenarios, is generated using a simulation method that combines power system planning and operational models. These models account for the future development of the power system, including generation-grid-load-storage co-optimization, and carbon emission flow calculations to derive high-resolution factors. The methodology's accuracy is validated by comparing simulated emission factors with official data, yielding Mean Absolute Percentage Errors (MAPEs) below 2.525%.

Scientific Data
Tsinghua Sichuan Energy Internet Research Institute (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Tsinghua University
Openalex Percentile: Top 17%
Integrated Energy Systems Optimization
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.