Evidence map›Paper›PMID 35954894›Full record

ArticleInternational journal of environmental research and public health2022

Carbon Dioxide Emissions Reduction through Technological Innovation: Empirical Evidence from Chinese Provinces.

Yanran Liu, Lei Tang, Guangfu Liu

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

3 authors.

Yanran LiuShanghai International College of Intellectual Property, Tongji University, Shanghai 200092, China.ORCID 0000-0002-6952-1416
Lei TangSchool of Economics and Management, Tongji University, Shanghai 200092, China.
Guangfu LiuSchool of Economics and Management, Tongji University, Shanghai 200092, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Energy consumption and industrial activities are the primary sources of carbon emissions. As the "world's factory" and the largest carbon emitter, China has been emphasizing the core role of technological innovation in promoting industrial structure upgrades (ISU) and energy efficiency (EE) to reduce carbon emissions from industrial production and energy consumption. This study investigated the mechanism (through ISU and EE) and spillover effect of technological innovation on carbon emission reduction using the panel dataset of 30 Chinese provinces from 2008 to 2019 and spatial econometrics models. The study concluded that (1) technological innovation had a negative direct effect on provincial carbon emissions, while it also showed a spatial spillover effect on neighboring provinces; (2) technological innovation had an indirect effect on provincial carbon emissions reduction through the mediation of energy efficiency improvement, while the mediation effect of industrial structure upgrading is not yet significant; and (3) the effect of technological innovation on carbon emission reduction showed heterogeneity in the eastern, central, and western regions of China. This study provided empirical and theoretical references to decision-makers in China and other developing countries in promoting technological and carbon control policies. More specifically, direct technology investment and indirect investment in industrial structure upgrades and energy efficiency could help with regional carbon emissions reduction.

Indexed as

Carbon DioxideTechnologyChinaEconomic DevelopmentIndustryInventionsInvestmentsCarbon Dioxidecarbon emission reductionempirical analysisspatial econometricsspatial mediation modeltechnological innovation

Identifiers

PMID35954894
PMCPMC9368629

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.