EconPapers    
Economics at your fingertips  
 

A small-sample hybrid model for forecasting energy-related CO2 emissions

Ming Meng, Dongxiao Niu and Wei Shang

Energy, 2014, vol. 64, issue C, 673-677

Abstract: Mitigating the impact of developing countries to global climate change has become an important issue in the fields of science and politics. This study proposes a small-sample hybrid model for forecasting the energy-related CO2 emissions of developing countries. The CO2 emissions of these countries have not reached the inflection point of the long-term S-shaped curve and usually present short-term linear or approximately exponential trends. This concern is considered in the design of a hybrid forecasting equation combined by a nonhomogeneous exponential equation and a linear equation. The estimated parameters of the hybrid equation are obtained by minimizing the residual sum of squares and solving for a non-constrained optimization equation. To evaluate the performance of the hybrid model, the traditional linear model, GM (grey model) (1, 1), and the hybrid model are used to forecast the CO2 emissions of China from 1992 to 2011. Analysis of forecasting results shows that the hybrid model can respond more quickly to changes in emission trends than can the two models because of the specialized equation structure. Overall error analysis indicators also show that hybrid model often obtains more precise forecasting results than do the other two models.

Keywords: Hybrid model; Energy-related CO2 emissions; Forecasting; Developing countries (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (4) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544213008669
Full text for ScienceDirect subscribers only

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:eee:energy:v:64:y:2014:i:c:p:673-677

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Series data maintained by Dana Niculescu ().

 
Page updated 2017-09-29
Handle: RePEc:eee:energy:v:64:y:2014:i:c:p:673-677