Evidence map›Paper›PMID 39570832›Full record

ArticlePloS one2024

Reducing carbon emissions in the cement industry using effective measures based on countries' characteristics.

Hui Gao, Donglin Wang, Zhongwei Zhao, Pei Dang

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hui GaoSchool of Civil Engineering, Liaoning Technical University, Fuxin, China.ORCID 0000-0001-5125-8528
Donglin WangSchool of Civil Engineering, Liaoning Technical University, Fuxin, China.ORCID 0009-0001-6138-8737
Zhongwei ZhaoSchool of Civil Engineering, Liaoning Technical University, Fuxin, China.
Pei DangSchool of Economics and Management, Tianjin Chengjian University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cement production contributes 5% of global anthropogenic CO2 emissions (CEs), and more than 90% of the CEs are in the procedure of pyroprocessing. Thus calculating the pyroprocessing CO2 emission number (PCEN), determining CE-impacted factors, and investigating tailored measures of PCEN reduction for countries based on their characteristics is quite necessary. More specifically, different countries can meet different obstacles to reducing PCENs, such as different restrictions on natural resources and policies, improper energy structures, and so on. With this in mind, tailored measures for PCEN reduction in different countries should be investigated and developed based on their own characteristics. This study selects four sample countries, China, the US, Australia, and Turkey, because of their representative locations and characteristics and then determines PCENs for these countries. The results show that the PCENs of China, the US, Australia, and Turkey are 884, 886, 828, and 913 kgCO2/t clinker, respectively. Subsequently, the most PCEN-impacted factors are analyzed, and the reasons for sample countries' different PCENs are discussed. Then, corresponding custom measures are proposed for each country based on its characteristics. The measures proposed in this study can help with the PCE reduction in the world's cement industry, and the collected data and calculated results can be used to further research on improving energy conservation and emission reduction measures.

Indexed as

Carbon DioxideConstruction MaterialsAir PollutantsAustraliaCarbonChinaTurkeyUnited StatesAir PollutantsCarbonCarbon Dioxide

Identifiers

PMID39570832
PMCPMC11581325

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.