Evidence map›Paper›PMID 35805488›Full record

ArticleInternational journal of environmental research and public health2022

Peaking Industrial CO

Haiyan Duan, Xize Dong, Pinlei Xie, Siyan Chen, Baoyang Qin, Zijia Dong, Wei Yang

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
  2. Article
  3. Article
  4. Is the R&D Expenditure of Listed Companies Green? Evidence from China's A-Share Market.International journal of environmental research and public health · 2022
    Article
  5. COInternational journal of environmental research and public health · 2022
    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

7 authors.

Haiyan DuanKey Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China.
Xize DongCollege of New Energy and Environment, Jilin University, Changchun 130021, China.
Pinlei XiePeople's Government of Daqiao Town, Jiangdu District, Yangzhou 225211, China.
Siyan ChenCollege of New Energy and Environment, Jilin University, Changchun 130021, China.
Baoyang QinCollege of New Energy and Environment, Jilin University, Changchun 130021, China.
Zijia DongCollege of New Energy and Environment, Jilin University, Changchun 130021, China.
Wei YangKey Lab of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China.ORCID 0000-0001-8097-9009

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Peaking industrial carbon dioxide (CO2) emissions is critical for China to achieve its CO2 peaking target by 2030 since industrial sector is a major contributor to CO2 emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO2 emissions, which also have huge CO2 emissions reduction potential. It is significant to accurately forecast CO2 emission peak of industrial sector in heavy industrial regions from multi-industry and multi-energy type perspectives. This study incorporates 41 industries and 16 types of energy into the Long-Range Energy Alternatives Planning System (LEAP) model to predict the CO2 emission peak of the industrial sector in Jilin Province, a typical heavy industrial region. Four scenarios including business-as-usual scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS) and low-carbon scenario (LCS) are set for simulating the future CO2 emission trends during 2018−2050. The method of variable control is utilized to explore the degree and the direction of influencing factors of CO2 emission in four scenarios. The results indicate that the peak value of CO2 emission in the four scenarios are 165.65 million tons (Mt), 156.80 Mt, 128.16 Mt, and 114.17 Mt in 2040, 2040, 2030 and 2020, respectively. Taking ELS as an example, the larger energy-intensive industries such as ferrous metal smelting will peak CO2 emission in 2025, and low energy industries such as automobile manufacturing will continue to develop rapidly. The influence degree of the four factors is as follows: industrial added value (1.27) > industrial structure (1.19) > energy intensity of each industry (1.12) > energy consumption types of each industry (1.02). Among the four factors, industrial value added is a positive factor for CO2 emission, and the rest are inhibitory ones. The study provides a reference for developing industrial CO2 emission reduction policies from multi-industry and multi-energy type perspectives in heavy industrial regions of developing countries.

Indexed as

Carbon DioxideIndustryChinaCommerceForecastingFossil FuelsCarbon DioxideFossil FuelsCO2 emissionindustrial sectorinfluencing factorLEAP modelpeak

Identifiers

PMID35805488
PMCPMC9266074

What OpenQuestion holds

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LicenceCC BY
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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.