Forecasting of Energy-Related CO 2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

Shuyu Dai, Dongxiao Niu and Yaru Han
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Shuyu Dai: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Dongxiao Niu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Yaru Han: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2018, vol. 10, issue 4, 1-17

Abstract: Presently, China is the largest CO 2 emitting country in the world, which accounts for 28% of the CO 2 emissions globally. China’s CO 2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO 2 emissions is crucial to China’s emission reduction policy formulating and global action on climate change. In order to forecast the CO 2 emissions in China accurately, considering population, the CO 2 emission forecasting model using GM(1,1) (Grey Model) and least squares support vector machine (LSSVM) optimized by the modified shuffled frog leaping algorithm (MSFLA) (MSFLA-LSSVM) is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO 2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1) model is used to forecast the main influencing factors of CO 2 emissions. Finally, taking the forecasting value of the CO 2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO 2 emissions in China from 2018 to 2025.

Keywords: CO 2 emissions forecasting; GM(1,1); least squares support vector machine; modified shuffled frog leaping algorithm; influencing factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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