PREDICTION OF INDUSTRIAL CARBON EMISSIONS IN SHAANXI PROVINCE BASED ON LASSO-GWO-KELM MODEL

ZHANG Xinsheng; WEI Zhizhen; CHEN Zhangzheng; HAN Yiwei

doi:10.13205/j.hjgc.202310018

Volume 41 Issue 10

Oct. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(10): 141-149

ZHANG Xinsheng, WEI Zhizhen, CHEN Zhangzheng, HAN Yiwei. PREDICTION OF INDUSTRIAL CARBON EMISSIONS IN SHAANXI PROVINCE BASED ON LASSO-GWO-KELM MODEL[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 141-149. doi: 10.13205/j.hjgc.202310018

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ZHANG Xinsheng, WEI Zhizhen, CHEN Zhangzheng, HAN Yiwei. PREDICTION OF INDUSTRIAL CARBON EMISSIONS IN SHAANXI PROVINCE BASED ON LASSO-GWO-KELM MODEL[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 141-149. doi: 10.13205/j.hjgc.202310018

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PREDICTION OF INDUSTRIAL CARBON EMISSIONS IN SHAANXI PROVINCE BASED ON LASSO-GWO-KELM MODEL

doi: 10.13205/j.hjgc.202310018

1. School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China;
2. The Research Institute of New Urbanization and Human Settlement in Shaanxi Province, Xi'an 710055, China

Received Date: 2023-07-08
Available Online: 2023-12-26

Abstract

Abstract

A model based on LASSO regression (LASSO), Grey Wolf Optimization algorithm (GWO) and nuclear Extreme Learning Machine (KELM) was established to improve the prediction accuracy of industrial carbon emissions. Firstly, the direct and indirect carbon emissions of the industry during 2000 to 2020 were calculated according to the IPCC formula method and electrothermal allocation method respectively, and the gross domestic product (GDP), energy structure, and fixed asset investment were selected by the STIRPAT model. Then seven significant influencing factors were selected by grey correlation analysis and the LASSO regression model. Secondly, the parameter data of the industrial carbon emission system were preprocessed and input into the KELM model, and the KELM regularization coefficient (C) and kernel function parameter (γ) were optimized using the GWO algorithm. Finally, the forecast results were integrated and summarized, and the forecast results of LASSO-GGO-KELM, LASSO-SSA-KELM, LASSO-SFO-KELM, LASSO-KELM, and LASSO-ELM were compared and analyzed. The results showed that the predicted value of the LASSO-GGO-KELM model fits well with the actual value, and its mean square error, mean absolute error, root mean square error, and mean absolute percentage error was 0.02%, 1.09%, 1.33%, and 1.17% respectively, which was superior to other models, proving that this model can predict industrial carbon emissions more accurately. This study can provide a reference for China to realize the Double Carbon Goal as soon as possible.
- industry,
- LASSO regression,
- kernel extreme learning machine (KELM),
- grey wolf optimization algorithm (GWO),
- carbon emission prediction

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References(27)

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