Evidence map›Paper›PMID 38192820›Full record

ArticleHeliyon2024

Environmental regulation, technology density, and green technology innovation efficiency.

Pengzhen Liu, Tao Huang, Yueting Shao, Bin Jia

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.

Pengzhen LiuSchool of Economics, Jinan University, Guangzhou 510632, China.
Tao HuangSchool of Economics, Jinan University, Guangzhou 510632, China.
Yueting ShaoSchool of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China.
Bin JiaSchool of Economics, Jinan University, Guangzhou 510632, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

From the perspective of the innovation value chain, this study divides the innovation efficiency of green technology into two stages: R&D efficiency and achievement transformation efficiency. Technology density is introduced as a threshold variable to examine the influence of environmental regulation on the efficiency of green technology innovation at both stages. The findings reveal that China's overall green technology innovation efficiency (GTIE) is improving. R&D efficiency initially declined, then increased, while the efficiency of achievement transformation experienced a three-stage pattern: rise-fall-rise. The GTIE distribution across the two stages progressively increases from the northwest to the southeast, resulting in a concentrated, contiguous "line-shaped" and "block-shaped" pattern. High-efficiency areas are primarily found in the eastern coastal regions. Nationally, Environmental regulation and R&D efficiency share an inverted U-shaped relationship, with a double threshold effect of technology density. Environmental regulation does not significantly affect achievement transformation efficiency, but there is a single threshold effect due to technology density.

Indexed as

Environmental regulationGreen technology innovation efficiencyInnovation value chainTechnical densityThreshold model

Identifiers

PMID38192820
PMCPMC10772212

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.