Issue |
EPL
Volume 146, Number 3, May 2024
|
|
---|---|---|
Article Number | 31002 | |
Number of page(s) | 7 | |
Section | Statistical physics and networks | |
DOI | https://doi.org/10.1209/0295-5075/ad397d | |
Published online | 07 May 2024 |
The influence patterns of carbon flux in different climatic zones in China —Based on the complex network approach
1 School of Mathematical Sciences, Jiangsu University - Zhenjiang, 212013 Jiangsu, China
2 Department of Physics, Bar-Ilan University - Ramat-Gan 52900, Israel
3 Institute of Carbon Neutral Development, School of Mathematical Sciences, Jiangsu University Zhenjiang 212013, Jiangsu, China
4 Jiangsu Province Engineering Research Center of Industrial Carbon System Analysis, School of Mathematical Sciences, Jiangsu University - Zhenjiang 212013, Jiangsu, China
5 Key Laboratory for NSLSCS, Ministry of Education, School of Mathematical Sciences, Nanjing Normal University Nanjing 210023, Jiangsu, China
6 Jiangsu Province Engineering Research Center of Spatial Big Data, School of Mathematical Sciences, Nanjing Normal University - Nanjing 210023, Jiangsu, China
Received: 6 February 2024
Accepted: 2 April 2024
Research on ecosystem carbon flux can provide important methodological and strategic support for climate change mitigation. The existing studies focus on the calculation of carbon flux, ignoring the intertwined effects between regions. The quantification and analysis of the interaction patterns of carbon flux is crucial for understanding the global carbon cycle process, forecasting and coping with climate change. In this study, carbon flux network model sequences are established based on complex network theory using carbon flux data spanning from December 1, 2005, to November 30, 2020. The time delay effect is introduced to accurately quantify the influence patterns of carbon flux within climate zones across China. The findings indicate that the probability distribution function of the link weights during the various seasons of each year exhibits a bimodal distribution with distinct positive and negative components. The delay probability distribution function reveals the significant impact of delay effects, which are especially pronounced and mostly significant long-term lag effects in nodes with negative weights. Further, the results of the interactions of carbon flux among climate zones demonstrate that changes in carbon flux in the plateau and southern temperate regions have either positive or negative impacts on other climate zones. Therefore, controlling carbon flux changes in these climatic zones can effectively optimize the distribution of carbon flux. The modeling framework and results presented in this paper provide useful insights for the regulation and distribution optimization of carbon flux in China.
© 2024 EPLA
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