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

Linghan Xu, Jiaqi Zhang, Qiuhui Zhang, Xinxing Zhou and Shanshan Yu

International Journal of Energy Technology and Policy, 2025, vol. 20, issue 1/2, 144-162

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. (search for similar items in EconPapers)
Date: 2025
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