LMDI-ARIMA Model for Carbon Emissions Analysis and Prediction in High Energy Consuming Industries with Emission Reduction Measures

In this paper, starting from the specific perspective of enterprises, the carbon emission calculation model is first used to calculate and organize the carbon emission data of three typical high energy consuming industries. Secondly, the Logarithmic Mean Divisia Index (LMDI) decomposition analysis method is used to calculate the contribution of fossil fuels in the carbon emissions of enterprise fuel combustion. After comparison, it is found that steel enterprises are most affected by this factor. At the same time, time series analysis is used to predict the future carbon emissions of different enterprises by Autoregressive Integrated Moving Average (ARIMA) model. Finally, with the help of the established LMDI-ARIMA model, hypothetical adjustments were made to the proportion of fossil fuels in steel enterprises and carbon emissions predictions were made, obtaining the expected carbon reduction that the steel industry can achieve by changing the proportion of clean energy use.

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Published
2024-11-29
DOI
https://doi.org/10.1109/ei264398.2024.10990805
Primary Topic
Energy Load and Power Forecasting
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article
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article

LMDI-ARIMA Model for Carbon Emissions Analysis and Prediction in High Energy Consuming Industries with Emission Reduction Measures

Xuchen Wang, Weitao Yao, Xiangmin Xu, Xu Xu et al.
Energy Load and Power Forecasting
article

LMDI-ARIMA Model for Carbon Emissions Analysis and Prediction in High Energy Consuming Industries with Emission Reduction Measures

Xuchen Wang, Weitao Yao, Xiangmin Xu, Xu Xu, Chang Liu, Bowen Zheng, Mingyue Zhou, Wen Chu
article en

Abstract

In this paper, starting from the specific perspective of enterprises, the carbon emission calculation model is first used to calculate and organize the carbon emission data of three typical high energy consuming industries. Secondly, the Logarithmic Mean Divisia Index (LMDI) decomposition analysis method is used to calculate the contribution of fossil fuels in the carbon emissions of enterprise fuel combustion. After comparison, it is found that steel enterprises are most affected by this factor. At the same time, time series analysis is used to predict the future carbon emissions of different enterprises by Autoregressive Integrated Moving Average (ARIMA) model. Finally, with the help of the established LMDI-ARIMA model, hypothetical adjustments were made to the proportion of fossil fuels in steel enterprises and carbon emissions predictions were made, obtaining the expected carbon reduction that the steel industry can achieve by changing the proportion of clean energy use.

North China Electric Power University (CN), China Aluminum International Engineering Corporation Limited (China) (CN), China Electric Power Research Institute, Xi’an Jiaotong-Liverpool University (CN)
Openalex Percentile: Top 72%
Energy Load and Power Forecasting
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LMDI-ARIMA Model for Carbon Emissions Analysis and Prediction in High Energy Consuming Industries with Emission Reduction Measures — Xuchen Wang, Weitao Yao, et al. · (2024) | TGRS Research Map | TGRS