Title: Peak carbon emission prediction of expressway toll stations using GRA-LSTM under the dual carbon background

Authors: Yali Liang; Zengli Fang; Fang Wang; Gaoling Li; Yongjian Guo

Addresses: Zhengzhou Institute of Transportation Co., Ltd., Zhengzhou, 450000, China ' Zhengzhou Institute of Transportation Co., Ltd., Zhengzhou, 450000, China ' Zhengzhou Institute of Transportation Co., Ltd., Zhengzhou, 450000, China ' Zhengzhou Institute of Transportation Co., Ltd., Zhengzhou, 450000, China ' Zhengzhou Institute of Transportation Co., Ltd., Zhengzhou, 450000, China

Abstract: In order to reduce the error of carbon emission peak prediction and shorten the prediction time, an expressway toll station carbon emission peak prediction method based on the GRA-LSTM model is proposed in the background of dual carbon. Firstly, analyse the dual carbon goals and the characteristics of sustainable development. Secondly, convert the energy consumption generated during the vehicle's payment process into the vehicle's carbon emissions data. Finally, use the grey correlation analysis (GRA) method based on the collected carbon emission data, to calculate the correlation degree between the factors affecting carbon emissions. Using the long short-term memory (LSTM) model to construct a carbon emission peak prediction model, and the output result is the carbon emission peak prediction result. The experimental results show that the proposed method can shorten the prediction time while reducing the prediction RSME.

Keywords: dual carbon background; GRA-LSTM model; expressway toll stations; carbon emissions; peak prediction.

DOI: 10.1504/IJETM.2025.144501

International Journal of Environmental Technology and Management, 2025 Vol.28 No.1/2/3, pp.76 - 90

Received: 16 Jan 2024
Accepted: 12 Jun 2024
Published online: 17 Feb 2025 *

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