Construction and analysis of China's carbon emission model based on machine learning.

Jian Sun, Xinzi Wang, Mengkun Liang, Xiaoru Ren, Xuezhi Liu

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Machine learning integrates region-specific microbial signatures to distinguish geographically adjacent populations within a province.Frontiers in microbiology · 2025
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

5 authors.

Jian SunSchool of Economics and Management, Beijing University of Chemical Technology, No. 15 Beisanhuan East Road, Chaoyang District, Beijing, 100029, China.

Xinzi WangState Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing, 100029, China.

Mengkun LiangSchool of Economics and Management, Beihang University, Beijing, 100191, China.

Xiaoru RenSchool of Economics and Management, Beijing University of Chemical Technology, No. 15 Beisanhuan East Road, Chaoyang District, Beijing, 100029, China.

Xuezhi LiuSchool of Economics and Management, Beijing University of Chemical Technology, No. 15 Beisanhuan East Road, Chaoyang District, Beijing, 100029, China. liuxz@mail.buct.edu.cn.

Funding

ESG research project in China's petrochemical and chemical industry No. HS20240021First-class Discipline Construction of Beijing University of Chemical Technology No. XK2023-10

6 · The paper itself

Abstract

To cope with the severe challenge of China's huge carbon emissions, this study introduced a comprehensive research paradigm, "modelling + SHAP analysis + scenario prediction," from the machine learning perspective. Firstly, by calculating the Spearman correlation coefficient, it is found that China's carbon emissions are significantly correlated with nine explanatory variables, such as the proportion of coal in total energy consumption and urbanization rate. Then, the contribution value of each explanatory variable in the optimal model is quantified by the SHAP method, and it is revealed that energy intensity and urbanization rate are the key factors affecting China's carbon emissions and have negative and positive effects, respectively. Finally, through the policy scenario simulation, it is found that China's carbon emissions will level off from 2022 to 2028 and peak in 2028, which is expected to be about 9.72 billion tons of carbon emissions by 2030.

Indexed as

Carbon emissionsImportance analysisInfluencing factorsMachine learningScenario forecasting

Identifiers

PMID40246960

DOI10.1038/s41598-025-91222-6

PMCPMC12006312

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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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.