Affiliation of Author(s):经济与管理学院
Journal:JOURNAL OF CLEANER PRODUCTION
Key Words:Grey multivariable model Background value Adjustment coefficient CO2 emissions Forecasting
Abstract:Forecasting CO2 emissions in China always has been of great significance as it could help the government to improve energy policies and plans. To this end, a novel grey multivariable model is designed in this paper. Compared with the conventional grey multivariable model, which has certain drawbacks of inaccurate prediction and poor adaptability that restrict their applications in practical cases, the proposed model can make three improvements: first, an optimized grey model having a modified background value is proposed to predict the trends of the driving variables. Second, the novel grey multivariable model is established, combined with the changing trends of driving variables. Third, the adjustment coefficient in the new model is optimized to obtain optimal values for the time response function. To demonstrate its efficacy, the proposed model is employed to reproduce and predict the CO2 emissions from fuel combustion compared with four benchmark models-the results show that the new model yields more accurate forecasting results than the competing models. Eventually, the new model will be used to quantify future Chinese CO2 emissions from fuel combustion from 2014 to 2020, and the forecasted results can provide a solid basis for formulating environmental policies and energy consumption plans. (C) 2017 Elsevier Ltd. All rights reserved.
ISSN No.:0959-6526
Translation or Not:no
Date of Publication:2017-09-20
Co-author:丁松,F70206679,Wang Junjie
Correspondence Author:dyg
Date of Publication:2017-09-20