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Top1. Introduction
In the context of today's global warming, achieving carbon neutrality has become a critically discussed topic of paramount importance (Zhao et al., 2022). Carbon neutrality refers to the goal of reaching net-zero emissions by reducing and offsetting carbon dioxide emissions to combat the threat of climate change. However, despite the widespread discussion and interest in the concept of carbon neutrality within the environmental sector, realizing this goal still presents a series of significant challenges and difficulties (Wu et al., 2022). Firstly, the idea of carbon neutrality involves reducing greenhouse gas emissions into the atmosphere, a challenge in and of itself. Industrial production, energy consumption, and transportation activities worldwide lead to substantial carbon dioxide emissions. Therefore, effectively monitoring, managing, and reducing these emissions poses an urgent issue. Secondly, forecasting these emissions is also an essential aspect of carbon neutrality. Yet, weather patterns, climatic conditions, and various human-induced factors can influence fluctuating emission levels, making accurate predictions extremely challenging. As such, there is a need to seek more precise and reliable methods for forecasting future emission trends. Lastly, reducing emissions requires the formulation and implementation of a series of effective policies and measures, which in turn, touch upon a broad spectrum of social and economic factors. Hence, ensuring sustainable socio-economic development while reducing emissions becomes a complex problem to address (Waheed et al., 2019). To tackle these challenges, many researchers and scientists have turned to deep learning and artificial intelligence technologies. Deep learning, a potent method within machine learning, can handle large-scale and intricate data, facilitating better emission monitoring, future trend predictions, and the proposition of effective emission reduction strategies. Significant advancements in emission monitoring and forecasting through deep learning offer renewed hope for achieving carbon neutrality (Li et al., 2021).
Deep learning has demonstrated immense potential in various aspects of the carbon neutrality domain, offering innovative solutions to achieve carbon-neutral objectives. As a compelling testament to this, one can observe the successful application of deep learning in the organization of sporting events, intelligent scheduling, and resource management (Wang et al., 2021). Sporting events are large-scale activities that attract millions of viewers globally. However, the organization and management of these events often involve significant energy consumption and carbon emissions (Somu et al., 2021). To mitigate the adverse environmental impacts of these events, deep learning technologies have been introduced to optimize resource utilization and carbon-neutral strategies for the events. By analyzing vast amounts of historical data and real-time information, deep learning algorithms can intelligently adjust the planning, scheduling, and energy consumption of these events, minimizing emissions and ensuring the sustainability of the events. These successful instances not only reduce the carbon footprint but also offer insights for other sectors, indicating the broad applicability of deep learning in carbon neutrality efforts (Amasyali & El-Gohary, 2018).