Evidence map›Paper›PMID 42014409›Full record

ArticleScientific data2026

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

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yaowang Li *Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, 610213, China.
Shixu Zhang *Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, 610213, China.
Weiran LiDepartment of Electrical Engineering, Tsinghua University, Beijing, 100084, China.
Yuliang LiuSichuan Energy Internet Research Institute, Tsinghua University, Chengdu, 610213, China.
Yuting QinSichuan Energy Internet Research Institute, Tsinghua University, Chengdu, 610213, China.
Hongyi WeiDepartment of Electrical Engineering, Tsinghua University, Beijing, 100084, China.
Chongqing KangSichuan Energy Internet Research Institute, Tsinghua University, Chengdu, 610213, China.
Ning ZhangSichuan Energy Internet Research Institute, Tsinghua University, Chengdu, 610213, China. ningzhang@tsinghua.edu.cn.
Ershun DuSichuan Energy Internet Research Institute, Tsinghua University, Chengdu, 610213, China. duershung@tsinghua.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52477103
6 · The paper itself

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%.

Identifiers

PMID42014409
PMCPMC13287579

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.