A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning

Yuhong Zhao, Ruirui Liu, Zhansheng Liu (), Liang Liu, Jingjing Wang and Wenxiang Liu
Additional contact information
Yuhong Zhao: Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Ruirui Liu: Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Zhansheng Liu: Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Liang Liu: Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Jingjing Wang: Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Wenxiang Liu: Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China

Sustainability, 2023, vol. 15, issue 8, 1-28

Abstract: Under the background of global warming and the energy crisis, the Chinese government has set the goal of carbon peaking and carbon neutralization. With the rapid development of machine learning, some advanced machine learning algorithms have also been applied to the control and prediction of carbon emissions due to their high efficiency and accuracy. In this paper, the current situation of machine learning applied to carbon emission prediction is studied in detail by means of paper retrieval. It was found that machine learning has become a hot topic in the field of carbon emission prediction models, and the main carbon emission prediction models are mainly based on back propagation neural networks, support vector machines, long short-term memory neural networks, random forests and extreme learning machines. By describing the characteristics of these five types of carbon emission prediction models and conducting a comparative analysis, we determined the applicable characteristics of each model, and based on this, future research ideas for carbon emission prediction models based on machine learning are proposed.

Keywords: macroscopic carbon emission; prediction model; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/8/6876/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/8/6876/ (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:jsusta:v:15:y:2023:i:8:p:6876-:d:1127383

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

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