Evidence map›Paper›PMID 38322975›Full record

ArticleHeliyon2024

Enhancing labor productivity as a key strategy for fostering green economic growth and resource efficiency.

Xie Yu, Azer Dilanchiev, Sidra Bibi

RetractedAbstract readRetracted Publication
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. It has been retracted, and should not be counted. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Xie YuParty School of CPC Shenzhen Longhua District Committee, Shenzhen, 518000, Guangdong, China.
Azer DilanchievSchool of Business, International Blacksea University, Georgia.
Sidra BibiSchool of Economics and Management, Chongqing Jiaotong University, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amidst a time when uncontrolled economic growth has frequently harmed the environment, it is crucial to reassess our strategy toward economic progress. The necessity to tackle climate change, resource depletion, and environmental deterioration demands a profound transition towards ecologically sound and sustainable economic development. This study examines the crucial significance of labor productivity in promoting sustainable economic growth and the effective utilization of resources in Asia, Europe, and South America from 1990 to 2020. To accomplish this, we utilized the Data Envelopment Analysis (DEA) methodology to examine a range of input and output characteristics thoroughly. These parameters included labor productivity, renewable energy usage, material efficiency, Green GDP, carbon footprint, and water footprint. The results of our study demonstrate significant regional variations in the efficient utilization of labor and resources to promote sustainable economic development. The findings of the DEA model emphasize that countries with higher labor productivity are more capable of pursuing an environmentally benign and sustainable financial path. Moreover, our research demonstrates a substantial association between enhanced labor productivity and diminished carbon and water footprints. This highlights the importance of labor productivity as a fundamental element for maximizing resource efficiency. In addition, we propose policy suggestions that motivate and improve worker efficiency as a practical strategy to accomplish both economic growth and environmental sustainability.

Indexed as

Environmental sustainabilityGreen economic growthLabour productivityResource efficiency

Identifiers

PMID38322975
PMCPMC10844128

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.