Evidence map›Paper›PMID 39752426›Full record

ArticlePloS one2025

Navigating technological innovation and rising costs: Assessing economic performance in Chinese manufacturing.

Songling Wu, Yanqi Si, Xiaoxiang Wang

Abstract read
In one paragraph

Article in PloS one, 2025. 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.

Songling WuBusiness College, Henan University of Science and Technology, Luoyang, China.
Yanqi SiBusiness College, Henan University of Science and Technology, Luoyang, China.ORCID 0009-0002-3098-9393
Xiaoxiang WangCollege of Economics and Management, Wenzhou University of Technology, Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Economic performance is an important indicator of the efficiency and quality of a company's production, which is closely related to the profitability of the company and is crucial for the development of the manufacturing industry.This paper aims to develop a theoretical framework for assessing economic performance within the Chinese manufacturing industry. It achieves this by incorporating inputs, outputs, and energy consumption costs into the production function. By analyzing manufacturing data from 2000 to 2021, it quantifies the impact of various factors, including labor costs and technological advancements, on economic performance. The findings highlight technological progress as the primary driver of economic growth within the Chinese manufacturing sector. Notably, there exists a U-shaped relationship between technical progress and economic performance, suggesting nuanced dynamics at play. Contrary to expectations, the rate of change in per capita wages shows no significant positive impact on economic performance. However, technical progress in the eastern and central regions exhibits a capital bias and positively influences economic performance. Similarly, a U-shaped relationship characterizes the relationship between the western region and manufacturing industry performance. These results underscore the crucial role of technological innovation in sustaining economic performance amid challenges such as rising labor and environmental costs. Emphasizing the reliance on scientific and technological progress emerges as imperative for enhancing the industry's economic resilience and growth.

Indexed as

Economic DevelopmentInventionsManufacturing IndustryChinaHumansIndustryModels, EconomicTechnology

Identifiers

PMID39752426
PMCPMC11698398

What OpenQuestion holds

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LicenceCC BY
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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.