Evidence map›Paper›PMID 34643866›Full record

ArticleEnvironmental science and pollution research international2022

A road map for environmental sustainability and green economic development: an empirical study.

Syed Abdul Rehman Khan, Zhang Yu, Muhammad Umar

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

3 authors.

Syed Abdul Rehman KhanSchool of Management and Engineering, Xuzhou University of Technology, Xuzhou, China. Sarehman_cscp@yahoo.com.ORCID http://orcid.org/0000-0001-5197-2318
Zhang YuSchool of Economics and Management, Chang'an University, Xi'an, China.
Muhammad UmarFaculty of Business Economics and Social Development, Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia.

Funding

beijing key laboratory of urban spatial information engineering 20210218
6 · The paper itself

Abstract

In today's era, the world economy needs to move towards a green transformation. Green total factor productivity provides the judgment about a country or region's ability to achieve long-term sustainable development goals. However, many factors considerably affect green total factor productivity that needs to be explored and clarified. This panel study investigates the link between technological input, environmental policies, governmental involvement, manufacturing and logistics industry cooperation, renewable energy consumption, and green total factor productivity in the context of Chinese's manufacturing and logistics industry. Hypotheses are tested through fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) econometric technique. The study used 12 cities data mainly taken from China Urban Statistical Yearbook (2005-2019) and National Economic and Social Development Statistics Bulletin. The results indicate that technological input, environmental policies, governmental involvement, manufacturing and logistics industry cooperation, and renewable energy consumption are significantly linked to green total factor productivity. The result also implies that the factors mentioned above have a crucial role in the transformation process. Moreover, the current research results will help popularize green total factor productivity and provide a new starting point for reducing non-renewable energy consumption and environmental pollution.

Indexed as

Carbon DioxideEconomic DevelopmentEnvironmental PollutionRenewable EnergySustainable DevelopmentCarbon DioxideEnvironmental policiesGreen economyRenewable energyTechnological input

Identifiers

What OpenQuestion holds

Textmetadata
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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.