Evidence map›Paper›PMID 35096760›Full record

ArticleFrontiers in public health2021

Green Growth, Green Technology, and Environmental Health: Evidence From High-GDP Countries.

Zahid Hussain, Bilal Mehmood, Muhammad Kaleem Khan, Raymondo Sandra Marcelline Tsimisaraka

Abstract read
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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.

Zahid HussainSchool of Finance, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
Bilal MehmoodSchool of Economics, Director Centre for Economics and Business Research, University of the Punjab, Lahore, Pakistan.
Muhammad Kaleem KhanAsia-Australia Business College, Liaoning University, Shenyang, China.
Raymondo Sandra Marcelline TsimisarakaBusiness School, Huanggang Normal University, Huanggang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Green growth is an exceptional strategy for sustainable development. It provides a pathway to combat environmental issues and the use of natural resources. This study investigates the effects of green technology and environmental factors on green growth in high-gross domestic product (GDP) countries from 2000 to 2020. In addition, it also probes the linear and nonlinear effects of GDP on green growth. To do so, we employ an advanced econometric approach, e.g., a cross-sectional autoregressive distributed lags estimator for long and short runs. The outcomes demonstrate that the linear effect of GDP is positive for green growth. On the contrary, the nonlinear effect of GDP has a negative magnitude for green growth. Besides, green technology substantially increases green growth. Energy consumption is found to be an important influencer, and it decreases green growth. Environmental factors such as emissions, according to the findings, also reduce green growth in the sample countries. It is worth noting that the joint effects of energy consumption and emissions deteriorate green growth in countries. Based on empirical findings, for policy makers, this study suggests that high-GDP countries should manage their economic and environmental activities in order to increase the amount of green growth that may protect the ecological environment.

Indexed as

Carbon DioxideEconomic DevelopmentCross-Sectional StudiesEnvironmental HealthGross Domestic ProductTechnologyCarbon Dioxideenergy consumptionenvironmentGDPgreen growthgreen technology

Identifiers

PMID35096760
PMCPMC8794811

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.