Evidence map›Paper›PMID 41483373›Full record

ArticleCarbon balance and management2026

Mathematical modeling of carbon dioxide emissions with GDP linkage: sensitivity analysis and optimal control strategy.

Hua Liu, Zhuoma Gangji, Yumei Wei, Jianhua Ye, Gang Ma

Abstract read
In one paragraph

Article in Carbon balance and management, 2026. 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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Hua LiuSchool of Mathematics and Computer Science, Northwest Minzu University, Lanzhou Gansu, 730030, China. jslh@xbmu.edu.cn.
Zhuoma GangjiSchool of Mathematics and Computer Science, Northwest Minzu University, Lanzhou Gansu, 730030, China.
Yumei WeiExperimental Teaching Deparment, Northwest Minzu University, Lanzhou, 730000, Gansu, China.
Jianhua YeSchool of Mathematics and Computer Science, Northwest Minzu University, Lanzhou Gansu, 730030, China.
Gang MaSchool of Mathematics and Computer Science, Northwest Minzu University, Lanzhou Gansu, 730030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change and global warming are among the most significant issues that humanity is currently facing, and also among the issues that pose the greatest threats to all mankind. These issues are primarily driven by abnormal increases in greenhouse gas concentrations. Mathematical modeling serves as a powerful approach to analyze the dynamic patterns of atmospheric carbon dioxide. In this paper, we established a mathematical model with four state variables to investigate the dynamic behavior of the interaction between atmospheric carbon dioxide, GDP, forest area and human population. Relevant theories were employed to analyze the system's boundedness and the stability of equilibrium points. The parameter values were estimated with the help of the actual data in China and numerical fitting was carried out to verify the results of the theoretical analysis. The Partial Rank Correlation Coefficient (PRCC) determines the sensitivity ofan input parameter to the output by measuring the correlation between a single input parameter and the model output. The sensitivity analysis of the compartments with respect to the model parameters was analyzed by using the PRCCand the Latin Hypercube Sampling test.The results indicate that the sensitivity of GDP-driven CO₂ emissions and GDP-governed atmospheric CO₂ concentration to the system is not significant. This implies that within the GDP-driven mitigation framework, the regulatory effect of GDP on atmospheric CO₂ concentration is relatively limited, and its significance is less pronounced than that of forests. Therefore, future relevant strategies should prioritize parameters with higher sensitivity (e.g., forestation). Apply the optimal control theory to regulate the atmospheric carbon dioxide level and provide the corresponding numerical fitting. Finally, corresponding discussions and suggestions were put forward with the help of the results of the theoretical analysis and numerical fitting.

Indexed as

Carbon dioxide(CO2)GDPMathematical modelOptimal controlSensitivity analysis

Identifiers

PMID41483373
PMCPMC12781274

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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.