Simulation of China’s Carbon Emission based on Influencing Factors

Haojia Kong, Lifan Shi, Dan Da, Zhijiang Li, Decai Tang and Wei Xing
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Haojia Kong: School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China
Lifan Shi: School of Business, Jiangsu Vocational College of Electronics and Information, Huai’an 223001, China
Dan Da: School of Business, Jiangsu Open University, Nanjing 210000, China
Zhijiang Li: School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Decai Tang: School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Wei Xing: Office of Financial Affairs, Hu’nan Moderen Logistics College, Changsha 410131, China

Energies, 2022, vol. 15, issue 9, 1-15

Abstract: China is one of the world’s largest energy consumers and carbon emitters, and the situation of carbon emission reduction is serious. This paper forecasts the future trend of China’s carbon emissions by constructing a system dynamics model of China’s carbon emissions. The results show that China cannot fulfill its commitment to peak its carbon emissions in 2030 as scheduled. Secondly, the Logarithmic Mean Divisia Index model (LMDI) was used to analyze the influencing factors of China’s carbon emissions. The contribution rates of the five factors to China’s carbon emissions are as follows: economic development (226.30%), technological innovation (−105.92%), industrial structure (−26.55%), population scale (11.44%) and energy structure (−5.28%). Finally, this paper formulates five carbon emission reduction paths according to the size and direction of various factors that affect China’s carbon emissions. The paths of carbon emission reduction were simulated by using the system dynamics model of China’s carbon emissions. It is found that technological innovation is the key pathway for China to realize its commitment to carbon emission reduction. Slowing economic growth will delay the arrival time of peak carbon emissions and increase the intensity of carbon emissions. Optimizing the industrial structure, reducing the population scale and adjusting the energy structure can reduce the peak and carbon emissions in China, but the effect is small.

Keywords: China’s carbon emissions; peak carbon emissions; system simulation; factor analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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