Evidence map›Paper›PMID 37767483›Full record

ArticleHeliyon2023

Analysis of low-carbon technology innovation efficiency and its influencing factors based on triple helix theory: Evidence from new energy enterprises in China.

Yu Guo, Giulia Bruno, Deming Zhang, Kaikai Han

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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.

Yu GuoSchool of Economics and Management, Anhui University of Science and Technology, Huainan, 232001, China.
Giulia BrunoDepartment of Management and Production Engineering, Politecnico di Torino, Torino, 10129, Italy.
Deming ZhangMaintenance Branch of State Grid Anhui Electric Power Co., Ltd., Hefei, 230001, China.
Kaikai HanShandong Kerui Petroleum Equipment Co., Ltd., Dongying, 257000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aim: Low-carbon technology innovation(L-CTI) is an essential way to realize the socio-economic transition to a low-carbon model. However, relatively few studies have been conducted on the calculation of low-carbon technology innovation efficiency(L-CTIE) and the identification of factors influencing it. The study intends to assess the L-CTIE of new energy enterprises(L-CTI-NEEs) and to analyze its influencing factors, so as to further improve the L-CTIE capability. Methods: Using the panel data of new energy enterprises(NEEs) in 2010-2020, DEA(BCC)-Malmquist-Tobit method is constructed to static and dynamic evaluate the L-CTIE of new energy enterprises(L-CTIE-NEEs), and analyze its influencing factors with triple helix theory. Results: During the study period, the L-CTIE among NEEs was quite different, and the Malmquist index change trend had phased characteristics. From the perspective of factors influencing innovation efficiency, technology integration capacity of enterprises, support intensity of government and cooperation scale of government-enterprises-universities & research institutions play a crucial part in promoting the EEFCH, PECH and SECH of L-CTIE-NEEs, while resource conversion capacity of universities & research institutions only promotes PECH. Conclusion: According to the research results, a triple helix model of L-CTI-NEEs is constructed to enhance its L-CTIE level.

Indexed as

DEA(BCC)-Malmquist index methodLow-carbon technology innovation efficiency(L-CTIE)New energy enterprises(NEEs)Panel tobit modelTriple helix

Identifiers

PMID37767483
PMCPMC10520818

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.