Evidence map›Paper›PMID 35854783›Full record

ArticleNational science review2022

Substantial transition to clean household energy mix in rural China.

Guofeng Shen, Rui Xiong, Yanlin Tian, Zhihan Luo, Bahabaike Jiangtulu, Wenjun Meng, Wei Du, Jing Meng, Yuanchen Chen, Bing Xue and 21 more

Abstract read
In one paragraph

Article in National science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. China's Low-Carbon Energy Transition Over the Past Two Decades: Experiences and Implications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Article
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  19. Article
  20. Threat of air pollution in the cleanest plateau.Innovation (Cambridge (Mass.)) · 2023
    Review
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

31 authors.

Guofeng ShenCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.ORCID https://orcid.org/0000-0002-7731-5399
Rui XiongCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
Yanlin TianCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
Zhihan LuoCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
Bahabaike JiangtuluInstitute of Reproductive and Child Health, Peking University, Beijing 100191, China.
Wenjun MengCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
Wei DuLaboratory of Geographic Information Science, School of Geographic Sciences, East China Normal University, Shanghai 200241, China.
Jing MengThe Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, UK.ORCID https://orcid.org/0000-0001-8708-0485
Yuanchen ChenCollege of Environment, Zhejiang University of Technology, Hangzhou 310014, China.
Bing XueInstitute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.
Bin WangInstitute of Reproductive and Child Health, Peking University, Beijing 100191, China.
Yonghong DuanCollege of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, China.
Jia DuoXinjiang Key Laboratory of Environmental Pollution and Bioremediation, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
Fenggui FanSchool of Geography and Tourism, Anhui University, Wuhu 241000, China.
Lei HuangSchool of Environment, Nanjing University, Nanjing 210033, China.
Tianzhen JuCollege of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China.
Fenggui LiuCollege of Geographical Science, Qinghai Normal University, Xining 810008, China.
Shunxin LiCollege of Chemistry, Chemical Engineering and Environment, Minnan Normal University, Zhangzhou 363000, China.
Xianli LiuSchool of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi 435003, China.
Yungui LiDepartment of Environmental Engineering, Southwest University of Science and Technology, Mianyang 621010, China.
Mu WangCollege of Food Science, Tibet Agricultural and Animal Husbandry University, Linzhi 860000, China.
Ying NanCollege of Geography and Ocean Sciences, Yanbian University, Yanji 133002, China.
Bo PanFaculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Yanfang PanCollege of Geography and Environmental Science, Henan University, Kaifeng 475001, China.
Lizhi WangCollege of Ecology and Environment, Hainan University, Haikou 570228, China.
Eddy ZengSchool of Environment, Jinan University, Guangzhou 510632, China.
Chao ZhanInstitute of Coastal Research, Ludong University, Yantai 264025, China.
Yilin ChenCollege of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China.
Huizhong ShenCollege of Environmental Science and Technology, Southern University of Science and Technology, Shenzhen 518055, China.
Hefa ChengCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
Shu TaoCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The household energy mix has significant impacts on human health and climate, as it contributes greatly to many health- and climate-relevant air pollutants. Compared to the well-established urban energy statistical system, the rural household energy statistical system is incomplete and is often associated with high biases. Via a nationwide investigation, this study revealed high contributions to energy supply from coal and biomass fuels in the rural household energy sector, while electricity comprised ∼20%. Stacking (the use of multiple sources of energy) is significant, and the average number of energy types was 2.8 per household. Compared to 2012, the consumption of biomass and coals in 2017 decreased by 45% and 12%, respectively, while the gas consumption amount increased by 204%. Increased gas and decreased coal consumptions were mainly in cooking, while decreased biomass was in both cooking (41%) and heating (59%). The time-sharing fraction of electricity and gases (E&G) for daily cooking grew, reaching 69% in 2017, but for space heating, traditional solid fuels were still dominant, with the national average shared fraction of E&G being only 20%. The non-uniform spatial distribution and the non-linear increase in the fraction of E&G indicated challenges to achieving universal access to modern cooking energy by 2030, particularly in less-developed rural and mountainous areas. In some non-typical heating zones, the increased share of E&G for heating was significant and largely driven by income growth, but in typical heating zones, the time-sharing fraction was <5% and was not significantly increased, except in areas with policy intervention. The intervention policy not only led to dramatic increases in the clean energy fraction for heating but also accelerated the clean cooking transition. Higher income, higher education, younger age, less energy/stove stacking and smaller family size positively impacted the clean energy transition.

Indexed as

clean heatingenergy transitionhousehold energy mixmodern energysustainable development

Identifiers

PMID35854783
PMCPMC9283105

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.