Prediction of Carbon Emissions Level in China’s Logistics Industry Based on the PSO-SVR Model

Liang Chen, Yitong Pan and Dongqing Zhang ()
Additional contact information
Liang Chen: College of Information Management, Nanjing Agricultural University, Nanjing 210031, China
Yitong Pan: College of Information Management, Nanjing Agricultural University, Nanjing 210031, China
Dongqing Zhang: College of Information Management, Nanjing Agricultural University, Nanjing 210031, China

Mathematics, 2024, vol. 12, issue 13, 1-13

Abstract: Adjusting the energy structure of various industries is crucial for achieving China’s carbon peak and carbon neutrality goals. Given the significant proportion of carbon emissions from the logistics industry in the tertiary sector, the research on predicting the carbon emissions of the logistics industry is of great significance for China to achieve its “Dual carbon” target. In this paper, the gray relational analysis (GRA) methodology is adopted to screen the influencing factors of carbon emissions in the logistics industry firstly. Then, the particle swarm optimization (PSO) algorithm was used to optimize the penalty coefficientand kernel function range parameter of the support vector regression (SVR) model (i.e. PSO- SVR model). The data from 2000 to 2021 regarding carbon emissions and related influencing factors in China’s logistics industry are analyzed, and the mean absolute percentage error (MAPE) of the PSO-SVR model is 0.82%, which shows that the proposed PSO-SVR model in this paper is effective. Finally, instructive suggestions are provided for China to achieve the “Dual Carbon” goal and upgrading of the logistics industry.

Keywords: carbon emissions prediction; gray relational analysis; logistics industry; PSO algorithm; support vector regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/13/1980/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/13/1980/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:13:p:1980-:d:1422964

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().