Next Article in Journal
The Economic Valuation of Change in the Quality of Rural Tourism Resources: Choice Experiment Approaches
Previous Article in Journal
Building Customer Loyalty in Rural Destinations as a Pre-Condition of Sustainable Competitiveness
Open AccessArticle

Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(4), 958; https://doi.org/10.3390/su10040958
Received: 26 February 2018 / Revised: 22 March 2018 / Accepted: 25 March 2018 / Published: 26 March 2018
Presently, China is the largest CO2 emitting country in the world, which accounts for 28% of the CO2 emissions globally. China’s CO2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO2 emissions is crucial to China’s emission reduction policy formulating and global action on climate change. In order to forecast the CO2 emissions in China accurately, considering population, the CO2 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 CO2 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 CO2 emissions. Finally, taking the forecasting value of the CO2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO2 emissions in China from 2018 to 2025. View Full-Text
Keywords: CO2 emissions forecasting; GM(1,1); least squares support vector machine; modified shuffled frog leaping algorithm; influencing factors CO2 emissions forecasting; GM(1,1); least squares support vector machine; modified shuffled frog leaping algorithm; influencing factors
Show Figures

Figure 1

MDPI and ACS Style

Dai, S.; Niu, D.; Han, Y. Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability. Sustainability 2018, 10, 958.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop