Evidence map›Paper›PMID 27044089›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2016

Potential escalation of heat-related working costs with climate and socioeconomic changes in China.

Yan Zhao, Benjamin Sultan, Robert Vautard, Pascale Braconnot, Huijun J Wang, Agnes Ducharne

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. China's public health initiatives for climate change adaptation.The Lancet regional health. Western Pacific · 2023
    Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Reductions in Labor Capacity from Intensified Heat Stress in China under Future Climate Change.International journal of environmental research and public health · 2020
    Article
  14. Heat and Adult Health in China.Population and environment · 2018
    Article
  15. Article
  16. Climate change, food, water and population health in China.Bulletin of the World Health Organization · 2016
    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.

Yan ZhaoInstitut Pierre Simon Laplace (IPSL), 75005 Paris, France; LOCEAN, UMR 7159, Unité mixte Universite Pierre et Marie Curie (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Developpement (IRD)-Muséum national d'Histoire naturelle (MNHN), Sorbonne Universités, 75005 Paris, France; yan.zhao@locean-ipsl.upmc.fr.
Benjamin SultanInstitut Pierre Simon Laplace (IPSL), 75005 Paris, France; LOCEAN, UMR 7159, Unité mixte Universite Pierre et Marie Curie (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Developpement (IRD)-Muséum national d'Histoire naturelle (MNHN), Sorbonne Universités, 75005 Paris, France;
Robert VautardInstitut Pierre Simon Laplace (IPSL), 75005 Paris, France; LSCE, UMR 1572, Unité mixte Joint Unit of Commissariat à l'energie atomique et aux energies alternatives (CEA)-CNRS-Universite de Versailles Saint-Quentin (UVSQ), 91191 Gif-sur-Yvette, France;
Pascale BraconnotInstitut Pierre Simon Laplace (IPSL), 75005 Paris, France; LSCE, UMR 1572, Unité mixte Joint Unit of Commissariat à l'energie atomique et aux energies alternatives (CEA)-CNRS-Universite de Versailles Saint-Quentin (UVSQ), 91191 Gif-sur-Yvette, France;
Huijun J WangNansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;
Agnes DucharneInstitut Pierre Simon Laplace (IPSL), 75005 Paris, France; METIS, UMR 7619, Unité mixte UPMC-CNRS-Ecole Pratique des Hautes Etudes (EPHE), Sorbonne Universités, 75005 Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global climate change will increase the frequency of hot temperatures, impairing health and productivity for millions of working people and raising labor costs. In mainland China, high-temperature subsidies (HTSs) are allocated to employees for each working day in extremely hot environments, but the potential heat-related increase in labor cost has not been evaluated so far. Here, we estimate the potential HTS cost in current and future climates under different scenarios of socioeconomic development and radiative forcing (Representative Concentration Pathway), taking uncertainties from the climate model structure and bias correction into account. On average, the total HTS in China is estimated at 38.6 billion yuan/y (US $6.22 billion/y) over the 1979-2005 period, which is equivalent to 0.2% of the gross domestic product (GDP). Assuming that the HTS standards (per employee per hot day) remain unchanged throughout the 21st century, the total HTS may reach 250 billion yuan/y in the 2030s and 1,000 billion yuan/y in 2100. We further show that, without specific adaptation, the increased HTS cost is mainly determined by population growth until the 2030s and climate change after the mid-21st century because of increasingly frequent hot weather. Accounting for the likely possibility that HTS standards follow the wages, the share of GDP devoted to HTS could become as high as 3% at the end of 21st century.

Indexed as

Hot TemperatureChinaClimate ChangeGross Domestic ProductHealth ExpendituresHumansOccupational HealthSocioeconomic FactorsANOVAclimate changeCMIP5GDPhigh-temperature subsidies

Identifiers

PMID27044089
PMCPMC4855586

What OpenQuestion holds

Textmetadata
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.