Analysis influence factors and forecast energy-related CO 2 emissions: evidence from Hebei
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Analysis influence factors and forecast energy-related CO2 emissions: evidence from Hebei Wei Sun & Junjian Zhang
Received: 26 May 2020 / Accepted: 17 September 2020 / Published online: 1 October 2020 # Springer Nature Switzerland AG 2020
Abstract With economic development and the acceleration of urbanization, China’s energy demand has gradually increased and brought a lot of energy-related CO2 emissions. Energy-related CO2 emissions are affected by a variety of factors. Quantifying the correlation between energy-related CO2 and driving factors and constructing the driving factor system are conducive to predict the future energy-related CO2 emissions and analyze the impact of driving factors. In this paper, the improved grey relational analysis (IGRA) was proposed to screen the influencing factors of energy-related CO2 emissions considering the sample difference, and the factor analysis (FA) was used to reduce dimensionality of the influencing factors. Then, a carbon dioxide emission forecasting model based on the bacterial foraging optimization algorithm (BFO) and the least square support vector machine (LSSVM) was proposed. Empirical analysis results of Hebei show that the LSSVM optimized BFO significantly improves the accuracy of energy-related CO2 emissions forecasting, and IGRAFA-BFOLSSVM model is significantly better than BP, PSOBP, SVM, and LSSVM models. The mean absolute percentage error (MAPE) of the proposed model is 0.374%. The forecasting results of the supplementary W. Sun : J. Zhang (*) Department of Economics and Management, North China Electric Power University, Baoding 071000, China e-mail: [email protected]
W. Sun e-mail: [email protected]
case show that the model has better generalization ability. In addition, education and technological progress have proven to be important drivers of energy-related CO2 emissions. Simultaneously, the research results can also offer more breakthrough points for policy makers to control carbon emissions. Keywords Energy-related CO2 emissions . Improved grey relational analysis . Least square support vector machine . Bacterial foraging optimization algorithm Abbreviations GDP Gross domestic product FAI Fixed asset investment LFR Local finance revenue TIETC Total import and export trade of customs SADR Savings deposit of residents PCDIP Per capita disposable income of the population PCCEP Per capita consumption expenditures of the population ENCOE Engle coefficient TOTP Total population RAEM Rate of employment AVWE Average wage of employees CPI Consumer price index NGIHE Number of general institutions of higher education NSHEI Number of students in higher education institutions EDUEX Education expenditure RDEX R&D expenditure
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Introduction As a major greenhouse gas, the increase in carbon dioxide emissions will lead to global warming, which will have adverse effects on humans such as the melting of glaciers and extreme weather. With economic grow
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