Evidence map›Paper›PMID 37858411›Full record

ArticleScience bulletin2023

Projecting future labor losses due to heat stress in China under climate change scenarios.

Liangliang Cheng, Kuiying Gu, Liang Zhao, Huibin Wang, John S Ji, Zhao Liu, Jianbin Huang, Yidan Chen, Xuejie Gao, Ying Xu and 6 more

Open access · hybridAbstract read
In one paragraph

Article in Science bulletin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
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

5 citing papers in PubMed, 24 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
  4. Review
  5. Impacts of climate change on labor productivity: a narrative review.Revista brasileira de medicina do trabalho : publicacao oficial da Associacao Nacional de Medicina do Trabalho-ANAMT
    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

16 authors at 10 institutions in 2 countries.

Liangliang ChengSchool of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Vanke School of Public Health, Tsinghua University, Beijing 100084, China.
Kuiying GuVanke School of Public Health, Tsinghua University, Beijing 100084, China; Institute of Healthy China, Tsinghua University, Beijing 100084, China.
Liang ZhaoState Key Laboratory of Numerical Modelling for Atmosphere Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
Huibin WangSchool of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
John S JiVanke School of Public Health, Tsinghua University, Beijing 100084, China.
Zhao LiuSchool of Linkong Economics and Management, Beijing Institute of Economics and Management, Beijing 100102, China.
Jianbin HuangBeijing Yanshan Earth Critical Zone National Research Station, University of Chinese Academy of Sciences, Beijing 101408, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China.
Yidan ChenState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
Xuejie GaoCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100084, China; Climate Change Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
Ying XuNational Climate Centre, China Meteorological Administration, Beijing 100081, China.
Can WangState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
Yong LuoDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China.
Wenjia CaiDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China.
Peng GongDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China; Department of Earth Sciences and Department of Geography, University of Hong Kong, Hong Kong 999077, China.
Wannian LiangVanke School of Public Health, Tsinghua University, Beijing 100084, China; Institute of Healthy China, Tsinghua University, Beijing 100084, China.
Cunrui HuangVanke School of Public Health, Tsinghua University, Beijing 100084, China; Institute of Healthy China, Tsinghua University, Beijing 100084, China. Electronic address: huangcunrui@tsinghua.edu.cn.
Tsinghua University · CNState Key Joint Laboratory of Environment Simulation and Pollution Control · CNVanke (China) · CNChina Meteorological Administration · CNChinese Academy of Sciences · CNInstitute of Atmospheric Physics · CNInstitute of Economics · CNSun Yat-sen University · CNUniversity of Chinese Academy of Sciences · CNUniversity of Hong Kong · HK

Funding

Wellcome Trust
6 · The paper itself

Abstract

Climate change is expected to increase occupational heat stress, which will lead to diminished work performance and labor losses worldwide. However, sub-regional analyses remain insufficient, especially for countries with a heterogeneous spatial distribution of working populations, industries and climates. Here, we projected heat-induced labor losses in China, by considering local climate simulations, working population characteristics and developing an exposure-response function suitable for Chinese workers. We showed that the annual heat-induced work hours lost (WHL), compared to the baseline of 21.3 billion hours, will increase by 121.1% (111.2%-131.1%), 10.8% (8.3%-15.3%), and -17.8% (-15.3%--20.3%) by the end of the century under RCP(Representative Concentration Pathways)8.5, RCP4.5, and RCP2.6, respectively. We observed an approximately linear upward trend of WHL under RCP8.5, despite the decrease in future working population. Notably, WHL will be most prominent in the southern, eastern and central regions, with Guangdong and Henan accounting for a quarter of national total losses; this is largely due to their higher temperature exposure, larger population size, and higher shares of vulnerable population in total employment. In addition, limiting global warming to 1.5 °C would yield substantial gains. Compared to RCP2.6, RCP4.5, and RCP8.5, all provinces can avoid an average of 11.8%, 33.7%, and 53.9% of annual WHL if the 1.5 °C target is achieved, which is equivalent to avoiding 0.1%, 0.6%, and 1.4% of annual GDP losses in China, respectively. This study revealed climate change will exacerbate future labor losses, and adverse impacts can be minimized by adopting stringent mitigation policies coupled with effective adaptation measures. Policymakers in each province should tailor occupation health protection measures to their circumstances.

Indexed as

Climate changeHeat stressLaborOccupational healthWork hours lost

Identifiers

PMID37858411
PMCPMC10694465
OpenAlexW4387336244

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.