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Mathematical Problems in Engineering
Volume 2014, Article ID 616312, 5 pages
http://dx.doi.org/10.1155/2014/616312
Research Article

A New Algorithm of Parameter Estimation for the Logistic Equation in Modeling CO2 Emissions from Fossil Fuel Combustion

1School of Economics and Management, North China Electric Power University, No. 619 Yonghua Street, Baoding, Hebei 071003, China
2Soft Science Research Base of Hebei Province, North China Electric Power University, Baoding, Hebei 071003, China
3School of Economics, Hebei University, Baoding, Hebei 071002, China

Received 29 March 2014; Accepted 10 July 2014; Published 20 July 2014

Academic Editor: Xian Liu

Copyright © 2014 Ming Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

CO2 emissions from fossil fuel combustion have been considered as the most important driving factor of global climate change. A complete understanding of the rules of CO2 emissions is warranted in modifying the climate change mitigation policy. The current paper advanced a new algorithm of parameter estimation for the logistic equation, which was used to simulate the trend of CO2 emissions from fossil fuel combustion. The differential equation of the transformed logistic equation was used as the beginning of the parameter estimation. A discretization method was then designed to input the observed samples. After minimizing the residual sum of squares and letting the summation of the residual be equal to 0, the estimated parameters were obtained. Finally, this parameter estimation algorithm was applied to the carbon emissions in China to examine the simulation precision. The error analysis indicators mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), maximal absolute percentage error (MaxAPE), and geometric mean relative absolute error (GMRAE) all showed that the new algorithm was better than the previous ones.