Evidence map›Paper›PMID 38318041›Full record

ArticleHeliyon2024

Impacts of workplace automation on energy poverty: The new challenge of achieving SDG 7 in the context of technological revolution.

Xiaoru Niu, Chao Li, Xiang Li, Yuhan Zhang

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Xiaoru NiuSchool of Mechanical, Electrical & Information Engineering, Shandong University, 180 Wenhuaxi Road, Weihai, 264209, China.
Chao LiBusiness School, Shandong University, 180 Wenhuaxi Road, Weihai, 264209, China.
Xiang LiBusiness School, Shandong University, 180 Wenhuaxi Road, Weihai, 264209, China.
Yuhan ZhangHSBC Business School, Peking University, University Town, Shenzhen, 518055, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper systematically examines how workplace automation impacts energy poverty from a demand-side perspective, revealing a new challenge for Sustainable Development Goal 7 (SDG 7) in the context of technological revolution. Our research demonstrates that workplace automation significantly increases household energy poverty. This finding is robust when using the instrumental variable approach to tackle endogeneity, as well as employing different automation and energy poverty measures, placebo tests, and machine learning methods for robustness checks. Automation's impact mechanism is that it reduces people's income and work-related social capital, thus exposing households to higher risks of energy poverty. Moreover, its consequences are more prominent for rural households, less educated people, non-migrants, those without labor contracts, non trade-union members, and out-of-system workers. Thus enhancing human capital, promoting free movement of workers, and providing better labor protection contribute to weakening the adverse impact of the technological shock. Meanwhile, we find that improving the price reasonability, stability, security and accessibility of energy supply can also mitigate the negative effects of workplace automation on household energy consumption. In the dual context of the fourth technological revolution promoting industrial automation as well as the increasing urgency to achieve SDG 7, findings of this paper have important policy implications.

Indexed as

Energy consumptionEnergy povertySustainable development goal 7Technological revolutionWorkplace automation

Identifiers

PMID38318041
PMCPMC10839614

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.