Title: A peak carbon emission prediction method for enterprises based on IoT blockchain and grey neural network

Authors: Linghan Xu; Jiaqi Zhang; Qiuhui Zhang; Xinxing Zhou; Shanshan Yu

Addresses: Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China ' Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China ' Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China ' Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China ' Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, 510000, China

Abstract: In order to solve the problems of low accuracy and poor carbon emission potential of traditional enterprise carbon emission peak prediction methods, this paper proposes an enterprise carbon emission peak prediction method based on a combination model of the internet of things (IoT), blockchain, and grey neural network. Firstly, use IoT technology to obtain carbon emission data of enterprises. Secondly, use the blockchain carbon trading model to analyse the factors affecting corporate carbon emissions. Then, with the help of a grey prediction model, the predicted carbon emissions of the enterprise are obtained through cumulative reduction. Finally, the grey neural network combination model is used to predict the peak carbon emissions of enterprises by taking cumulative emissions as input. The experimental results show that the accuracy of the carbon emission peak prediction method in this article can reach 99.9%, which can effectively improve the prediction effect of enterprise carbon emission peaks.

Keywords: carbon trading model; grey prediction model; internet of things blockchain; BP neural network; cumulative reduction.

DOI: 10.1504/IJETP.2025.144308

International Journal of Energy Technology and Policy, 2025 Vol.20 No.1/2, pp.144 - 162

Received: 28 Apr 2024
Accepted: 08 Jul 2024
Published online: 05 Feb 2025 *

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