Evidence map›Paper›PMID 40467594›Full record

ArticleNature communications2025

Spatiotemporal assessment of renewable adequacy during diverse extreme weather events in China.

Kai Jiang, Nian Liu, Kunyu Wang, Yubing Chen, Jianxiao Wang, Yu Liu

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Kai JiangState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China.ORCID http://orcid.org/0000-0002-3543-8825
Nian LiuState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China. nianliu@ncepu.edu.cn.
Kunyu WangState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China.
Yubing ChenState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China.
Jianxiao WangNational Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China. wang-jx@pku.edu.cn.ORCID http://orcid.org/0000-0001-9871-5263
Yu LiuCollege of Urban and Environmental Sciences, Peking University, Beijing, China. yu.liu@pku.edu.cn.ORCID http://orcid.org/0000-0003-4605-0110

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

China has one of the world's largest wind and solar energy capacity worldwide; however, frequent extreme weather events remain major challenges to power systems. This study provides a nuanced examination of power balance issues on seven extreme weather types that occurred from 2020 to 2050. Utilizing an advanced metric for renewable adequacy and a comprehensive dataset comprising 1,089,891 alert records from 2843 counties, the results indicate that both adequacy surplus and deficit coexist in power systems under extreme weather conditions. In 2020, extreme weather led to a reduction in renewable energy by 31.79 TWh. Projections indicate this could rise to 263.61 TWh by 2050, equivalent to the combined annual electricity consumption in 2020 of Beijing and Shanghai, China's two largest cities. An extensive analysis that includes 4 evolutionary paths and 45 load conditions suggests that the average demand for provincial flexibility during extreme weather is projected to peak at 11.03 GW in 2050.

Identifiers

PMID40467594
PMCPMC12137629

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Registered trials

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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.