Simulation of carbon peaking process of high energy consuming manufacturing industry in Shaanxi Province: A hybrid model based on LMDI and TentSSA-ENN

Ke Hou; Jianping Sun; Minggao Dong; He Zhang; Qingqing Li; Ke Hou; Jianping Sun; Minggao Dong; He Zhang; Qingqing Li

doi:10.3934/mbe.2023819

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 10: 18445-18467. doi: 10.3934/mbe.2023819

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Simulation of carbon peaking process of high energy consuming manufacturing industry in Shaanxi Province: A hybrid model based on LMDI and TentSSA-ENN

1.
School of Economics and Management, Xi'an Shiyou University, Xi'an 710065, China
2.
School of Civil Engineering, Xi'an Shiyou University, Xi'an 710065, China
3.
Department of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA 70503, USA

Received: 10 August 2023 Revised: 14 September 2023 Accepted: 18 September 2023 Published: 25 September 2023

To achieve the goals of carbon peaking and carbon neutrality in Shaanxi, the high energy consuming manufacturing industry (HMI), as an important contributor, is a key link and important channel for energy conservation. In this paper, the logarithmic mean Divisia index (LMDI) method is applied to determine the driving factors of carbon emissions from the aspects of economy, energy and society, and the contribution of these factors was analyzed. Meanwhile, the improved sparrow search algorithm is used to optimize Elman neural network (ENN) to construct a new hybrid prediction model. Finally, three different development scenarios are designed using scenario analysis method to explore the potential of HMI in Shaanxi Province to achieve carbon peak in the future. The results show that: (1) The biggest promoting factor is industrial structure, and the biggest inhibiting factor is energy intensity among the drivers of carbon emissions, which are analyzed effectively in HMI using the LMDI method. (2) Compared with other neural network models, the proposed hybrid prediction model has higher accuracy and better stability in predicting industrial carbon emissions, it is more suitable for simulating the carbon peaking process of HMI. (3) Only in the coordinated development scenario, the HMI in Shaanxi is likely to achieve the carbon peak in 2030, and the carbon emission curve of the other two scenarios has not reached the peak. Then, according to the results of scenario analysis, specific and evaluable suggestions on carbon emission reduction for HMI in Shaanxi are put forward, such as optimizing energy and industrial structure and making full use of innovative resources of Shaanxi characteristic units.
- carbon emission prediction,
- LMDI,
- neural network,
- sparrow search algorithm,
- scenario analysis
Citation: Ke Hou, Jianping Sun, Minggao Dong, He Zhang, Qingqing Li. Simulation of carbon peaking process of high energy consuming manufacturing industry in Shaanxi Province: A hybrid model based on LMDI and TentSSA-ENN[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18445-18467. doi: 10.3934/mbe.2023819

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Abstract

To achieve the goals of carbon peaking and carbon neutrality in Shaanxi, the high energy consuming manufacturing industry (HMI), as an important contributor, is a key link and important channel for energy conservation. In this paper, the logarithmic mean Divisia index (LMDI) method is applied to determine the driving factors of carbon emissions from the aspects of economy, energy and society, and the contribution of these factors was analyzed. Meanwhile, the improved sparrow search algorithm is used to optimize Elman neural network (ENN) to construct a new hybrid prediction model. Finally, three different development scenarios are designed using scenario analysis method to explore the potential of HMI in Shaanxi Province to achieve carbon peak in the future. The results show that: (1) The biggest promoting factor is industrial structure, and the biggest inhibiting factor is energy intensity among the drivers of carbon emissions, which are analyzed effectively in HMI using the LMDI method. (2) Compared with other neural network models, the proposed hybrid prediction model has higher accuracy and better stability in predicting industrial carbon emissions, it is more suitable for simulating the carbon peaking process of HMI. (3) Only in the coordinated development scenario, the HMI in Shaanxi is likely to achieve the carbon peak in 2030, and the carbon emission curve of the other two scenarios has not reached the peak. Then, according to the results of scenario analysis, specific and evaluable suggestions on carbon emission reduction for HMI in Shaanxi are put forward, such as optimizing energy and industrial structure and making full use of innovative resources of Shaanxi characteristic units.

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