Forecasting carbon emissions using a multi-variable GM (1,N) model based on linear time-varying parameters

Article type: Research Article

Authors: Xiong, Pingping^{a; b; *} | Xiao, Lushuang^{b; c} | Liu, Yuchun^{b; c} | Yang, Zhuo^{b; c} | Zhou, Yifan^{b; c} | Cao, Shuren^{b; c}

Affiliations: [a] School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China | [b] Jiangsu Statistical Science Research Base, Nanjing University of Information Science and Technology, Nanjing, China | [c] College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, China

Correspondence: [*] Corresponding author. Pingping Xiong, E-mail: [email protected].

Abstract: Faced with serious growing global warming problem, it is important to predict carbon emissions. As there are a lot of factors affecting carbon emissions, a novel multi-variable grey model (GM(1,N) model) based on linear time-varying parameters discrete grey model (TDGM(1,N)) has been established. In this model, linear time-varying function is introduced into the traditional model, and dynamic optimization of fixed parameters which can only be used for static analysis is carried out. In order to prove the applicability and effectiveness of the model, this paper compared the model with the traditional model and simulated the carbon emissions of Anhui Province from 2005 to 2015. Carbon emissions in the next two years are also predicted. The results show that the TDGM(1,N) model has better simulation effect and higher prediction accuracy than the traditional GM(1,N) model and the multiple regression model(MRM) in practical application of carbon emissions prediction. In addition, the novel model of this paper is also used to predict the carbon emissions in 2018–2020 of Anhui Province.

Keywords: Linear time-varying parameters, grey system theory, multi-variable model, carbon emissions, forecasting

DOI: 10.3233/JIFS-202711

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6137-6148, 2021