Evidence map›Paper›PMID 35627591›Full record

ArticleInternational journal of environmental research and public health2022

Understanding the Heterogeneous Impact of Innovation Efficiency on Urban Ecological Footprint in China.

Hui Zhang, Haiqian Ke

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. 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

2 authors.

Hui ZhangNanyang Institute of Technology, Fanli Business School, Nanyang 473000, China.
Haiqian KeNanyang Institute of Technology, Fanli Business School, Nanyang 473000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Under the background of tightening resource constraints and a deteriorating ecological environment, innovation is aimed at saving energy, reducing consumption, abating pollution and achieving sustainable economic growth. This has gradually become an important way to improve industrial structure, competitiveness and environmental performance worldwide. In this study, we use the super-efficiency SBM model to calculate the innovation efficiency of 283 cities in China from 2009 to 2019. Then, based on the dynamic threshold regression model, we explore the impact of innovation efficiency on ecological footprint in innovative cities or non-innovative cities under different economic development levels. The main conclusions that can be drawn are as follows. (1) Within the research period, the influence of innovation efficiency on ecological footprint in China shows a negative double threshold feature, that is, increasing regional innovation efficiency has an inhibitory effect on ecological footprint. (2) For innovative cities, innovation efficiency has a strong inhibitory effect on ecological footprint, and it becomes stronger and stronger with the growth of night light data; but this inhibitory effect is gradually decreasing with improvement of economic development level in non-innovative cities. (3) Under the threshold of different levels of economic development, the number of scientific human resources, scientific financial resources, scientific information resources and scientific papers has a positive effect on ecological footprint, while the number of patent applications has a negative effect on ecological footprint.

Indexed as

Conservation of Natural ResourcesEconomic DevelopmentChinaCitiesEfficiencyHumansdynamic threshold effectecological footprintinnovation efficiencynight light data

Identifiers

PMID35627591
PMCPMC9140786

What OpenQuestion holds

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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.