Assessing the carbon footprint of soccer events through a lightweight CNN model utilizing transfer learning in the pursuit of carbon neutrality

IntroductionSoccer events require a lot of energy, resulting in significant carbon emissions. To achieve carbon neutrality, it is crucial to reduce the cost and energy consumption of soccer events. However, current methods for cost minimization often have high equipment requirements, time-consuming...

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Veröffentlicht in:Frontiers in ecology and evolution Jg. 11
Hauptverfasser: Liu, Zhewei, Guo, Dayong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Frontiers Media S.A 24.07.2023
Schlagworte:
attention mechanism
carbon neutral
CNN
energy saving and emission reduction
GRU
soccer game
ISSN:2296-701X, 2296-701X
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Zusammenfassung:IntroductionSoccer events require a lot of energy, resulting in significant carbon emissions. To achieve carbon neutrality, it is crucial to reduce the cost and energy consumption of soccer events. However, current methods for cost minimization often have high equipment requirements, time-consuming training, and many parameters, making them unsuitable for real-world industrial scenarios. To address this issue, we propose a lightweight CNN model based on transfer learning to study cost minimization strategies for soccer events in a carbon-neutral context.MethodsOur proposed lightweight CNN model uses a downsampling module based on the human brain for efficient information processing and a transfer learning-based module to speed up the training progress. We conducted experiments to evaluate the performance of our model and compared it with existing models in terms of the number of parameters and computation and recognition accuracy.ResultsThe experimental results show that our proposed network model has significant advantages over existing models in terms of the number of parameters and computation while achieving higher recognition accuracy than conventional models. Our model effectively predicts soccer event data and proposes more reasonable strategies to optimize event costs and accelerate the realization of carbon neutral goals.DiscussionOur proposed lightweight CNN model based on transfer learning is a promising method for studying cost minimization strategies for soccer events in a carbon-neutral context. The use of a downsampling module based on the human brain and a transfer learning-based module allows for more efficient information processing and faster training progress. The results of our experiments indicate that our model outperforms existing models and can effectively predict soccer event data and propose cost optimization strategies. Our model can contribute to the realization of carbon-neutral goals in the sports industry.
ISSN:2296-701X
2296-701X
DOI:10.3389/fevo.2023.1208643