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Prediction of energy‐related CO 2 emissions in multiple scenarios using a least square support vector machine optimized by improved bat algorithm: a case study of China
Author(s) -
Wu Qunli,
Meng Fanxing
Publication year - 2020
Publication title -
greenhouse gases: science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 32
ISSN - 2152-3878
DOI - 10.1002/ghg.1939
Subject(s) - cointegration , granger causality , china , fossil fuel , support vector machine , greenhouse gas , algorithm , econometrics , environmental science , engineering , computer science , mathematics , machine learning , ecology , biology , political science , law , waste management
At present, China has the world's highest CO 2 emissions. The reduction of China's CO 2 emissions will have a direct effect on the world. Considering that CO 2 emissions mainly come from the burning of fossil fuel, it is of great significance to accurately calculate and forecast China's energy‐related CO 2 emissions. To improve the prediction accuracy of CO 2 emissions, this paper proposed a new prediction model, which combines t‐distribution, Gaussian perturbations bat algorithm, and a least squares support vector machine, namely the TBAG‐LSSVM model. Furthermore, in order to ensure the rationality of factor selection, a stationary test, cointegration test, and Granger causality test were utilized to analyze factors affecting CO 2 emissions. Through an empirical test, it was found that the proposed model has higher accuracy than LSSVM, BA‐LSSVM, WOA‐LSSVM, ELM, and BPNN. Therefore, three scenarios were put into the model to predict China's CO 2 emissions up to 2030. The results indicate that CO 2 emissions in 2030 will be 12 853.18, 11 378.27, and 12 008.19 million tons. According to the simulation results, industrial restructuring and the elimination of outdated production capacity should be pushed forward continuously to ensure that the Chinese government achieves emission reduction targets. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.