Abstract: Carbon footprint measurement and estimation are important indicators for low-carbon supply chain assessment, but currently there is a lack of unified carbon footprint measurement standards and boundaries, and traditional carbon footprint measurement methods require significant computational costs. Therefore, a two-stage full lifecycle carbon footprint estimation method that calculates first and predicts later has been proposed. In the first stage, the power grid material supply chain is divided into five stages, and corresponding calculation models are constructed to achieve quantitative description and evaluation of carbon footprint; In the second stage, a supply chain lifecycle carbon emission prediction model based on long short-term memory neural network (LSTM) was constructed using cable products as carbon sources. Finally, numerical experiments were conducted based on carbon footprint management data of the power grid supply chain from 2020 to 2023, with a prediction accuracy of 99.3%. By comparing the models constructed with BP neural network and GABP neural network, the accuracy and superiority of the models have been demonstrated, achieving effective accounting and prediction of carbon footprint.

Key words: low carbon supply chain, carbon footprint measurement, carbon emission prediction, life cycle assessment, long short-term memory neural network (LSTM)