Evidence map›Paper›PMID 42437025›Full record

ArticleEnvironmental science and ecotechnology2026

Labor mobility buffers cascading supply-chain losses from occupational heat stress.

Qianzi Wang, Qi Zhou, Shen Qu

Abstract read
In one paragraph

Article in Environmental science and ecotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Qianzi WangSchool of Management, Beijing Institute of Technology, Beijing, 100081, China.
Qi ZhouSchool of Management, Beijing Institute of Technology, Beijing, 100081, China.
Shen QuSchool of Management, Beijing Institute of Technology, Beijing, 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Escalating global temperatures threaten economic stability by worsening occupational heat stress and reducing workforce productivity. Despite advancements in macroeconomic modeling, current risk assessments rely on coarse annual aggregations and ignore internal labor mobility, thereby masking highly unequal sub-national vulnerabilities and underestimating how labor mobility buffers cascading supply-chain losses. Here we present a high-resolution, agent-based dynamic supply chain network model that integrates empirical daily mobility data across 313 Chinese cities to quantify the spatiotemporal cascading economic impacts of occupational heat exposure. We show that annual heat stress costs China 2933.5 billion CNY (2.6% of GDP), with systemic propagation through supply chains driving 59% of these losses. Crucially, labor mobility redistributes risk: net labor inflows into industrialized, high-heat southeastern regions provide a factor-compensation effect that buffers cascading losses by offsetting direct local productivity shocks, saving a net 7.2 billion CNY directly and 24.6 billion CNY indirectly nationwide. Under a 2030 warming scenario (SSP3-7.0), total losses expand 1.6-fold to 4672.9 billion CNY, though integrated multi-level adaptations-combining industrial restructuring with work-hour shifting-can mitigate these future losses by 30%. These findings reveal that demographic mobility dictates the economic geometry of climate vulnerability, highlighting that resilient climate adaptation requires synchronized network-level interventions rather than isolated local policies.

Indexed as

Agent-based modelComplex supply chain networkIndirect economic lossOccupational heat stress

Identifiers

PMID42437025
PMCPMC13355723

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.