Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode

Yelin Wang, Ping Yang, Zan Song, Julien Chevallier and Qingtai Xiao ()
Additional contact information
Yelin Wang: Guangdong University of Technology
Ping Yang: Kunming University of Science and Technology
Zan Song: University of Nottingham
Julien Chevallier: IPAG Business School (IPAG Lab)
Qingtai Xiao: Kunming University of Science and Technology

Computational Economics, 2024, vol. 63, issue 2, No 10, 740 pages

Abstract: Abstract CO2 emissions have contributed to global warming and belong to high-noise, non-stationary and nonlinear systems. An accurate prediction method for annual CO2 emissions can improve the effectiveness of emission reduction policies. However, the existing prediction methods for small-scaled samples (i.e., hourly or daily time series) are unsuitable for regional policy benchmarks. Hence, a novel hybrid prediction model under data decomposition mode is developed for annual CO2 emissions in this work. For illustration, the five representative CO2 emissions (i.e., China, United States, India, Russian, and Japan) from 1970 to 2019 are collected to verify performance, which are taken from Global Carbon Project. The results show that the average prediction accuracy of the proposed prediction model is up to 97.95%, which whole performance improved by more than 1.61% compared with others. The total of five countries’ annual CO2 emissions in 2020 (18,311.72 metric tons) is approximately equal to that in 2018 (18,353.63 metric tons). The proposed model is a reliable prediction tool for annual CO2 emissions and can assist policymakers in adjusting reduction measures and regulators to access the current effects.

Keywords: Hybrid prediction model; CO2 emissions; Non-stationary and nonlinear system; De-noising; Empirical mode decomposition; Data decomposition mode (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-023-10357-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:kap:compec:v:63:y:2024:i:2:d:10.1007_s10614-023-10357-8

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-023-10357-8

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().