Proceedings of the
The Nineteenth International Conference on Computational Intelligence and Security (CIS 2023)
December 1 – 4, 2023, Haikou, China
Construction of Regional Carbon Emission Model Based on Random Forest and Grey Relational Analysis
School of Science, Changchun University, China.
ABSTRACT
The dual carbon target has an essential impact on China's ecological environment. It not only responds to China's green and sustainable development but also is an inevitable requirement for improving the living environment of residents. This article takes a southeast coastal region in China as an example to construct a carbon emission model. Firstly, starting from the correlation between regional carbon emissions, economy, population, energy consumption, and carbon emission factors, dividing the indicators into first, second, third, and fourth levels. This paper uses a random forest model to screen the indicators and construct a regional carbon emission indicator system based on feature weights. Secondly, the correlation model and grey correlation analysis model were used to analyze the correlation relationships between various indicators. Thirdly, we evaluated the accuracy of building an indicator system before and after screening variables and found significant improvement in each evaluation indicator. Finally, the chapter summarizes the results of this study and discusses the difficulties encountered in achieving carbon peaking and carbon neutrality in this region. Basing on the constructed carbon emission indicator system, this paper puts forward corresponding suggestions. It looks for improvement indicators to provide a basis for energy optimization in this region to achieve carbon peak and carbon neutrality as soon as possible.
Keywords: Carbon peak, Carbon neutrality, Carbon emissions, Random forest, Indicator system, Grey relational analysis.
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School of Science, Changchun University, China.
ABSTRACT
The dual carbon target has an essential impact on China's ecological environment. It not only responds to China's green and sustainable development but also is an inevitable requirement for improving the living environment of residents. This article takes a southeast coastal region in China as an example to construct a carbon emission model. Firstly, starting from the correlation between regional carbon emissions, economy, population, energy consumption, and carbon emission factors, dividing the indicators into first, second, third, and fourth levels. This paper uses a random forest model to screen the indicators and construct a regional carbon emission indicator system based on feature weights. Secondly, the correlation model and grey correlation analysis model were used to analyze the correlation relationships between various indicators. Thirdly, we evaluated the accuracy of building an indicator system before and after screening variables and found significant improvement in each evaluation indicator. Finally, the chapter summarizes the results of this study and discusses the difficulties encountered in achieving carbon peaking and carbon neutrality in this region. Basing on the constructed carbon emission indicator system, this paper puts forward corresponding suggestions. It looks for improvement indicators to provide a basis for energy optimization in this region to achieve carbon peak and carbon neutrality as soon as possible.
Keywords: Carbon peak, Carbon neutrality, Carbon emissions, Random forest, Indicator system, Grey relational analysis.
Download PDF