Artificial intelligence carbon neutrality strategy in sports event management based on STIRPAT-GRU and transfer learning - Zhang, Ying (Frontiers in Ecology and Evolution, 2023)
With the increasing concern about carbon emissions and climate change, carbon neutrality has become a crucial issue in various domains, including sports event management. Artificial intelligence has shown great potential in addressing environmental challenges and improving sustainability. To develop AI-powered carbon neutrality strategies for sports event managementthe ,this paper proposed a method based on STIRPAT-GRU and transfer learning. The primary objective of this research is to analyze the impact of population, wealth, and technology on carbon emissions in sports event management and to predict future trends and patterns of carbon emissions. Firstly, the STIRPAT model is used to identify the key drivers of carbon emissions in sports events. Then, the GRU neural network is then used to develop a predictive model to forecast future trends and patterns of carbon emissions. Finally, transfer learning is used to improve the accuracy and robustness of the model by leveraging knowledge and experience gained from other domains.The experimental results show that the proposed approach outperforms other methods, demonstrating its effectiveness in predicting carbon emissions in sports events and developing a carbon neutrality strategy. The significance of this research lies in its potential to provide sports event managers with a data-driven approach to manage carbon emissions, leading to a more sustainable and environmentally friendly sports industry.