Artificial Intelligence and Sustainability in Sports: Optimizing Resource Management in Mega Events

Abstract

Mega Sporting Events (MSEs), such as the Olympic Games and FIFA World Cup, attract worldwide interest but produce extreme environmental and resource management difficulties owing to the large infrastructure requirements, short project cycles, and large number of spectators. Increasing demands for environmentally sustainable behavior have made sustainability one of the primary focuses of event organizers. To investigate the role of AI in sustainability outcomes in Mega Sporting Events, this study uses a systematic scoping review. The PRISMA-ScR guideline is used in this study to collect data from literature in various databases such as Scopus, Web of Science, IEEE Xplore, Science Direct, and Google Scholar. The databases were searched using the terms "Artificial Intelligence," "Machine Learning," "Mega Sporting Event," "Sustainability," "Energy Management," "Crowd Management," and "Waste Reduction." English-language documents from 2010 through 2024 were selected, including only verified peer-reviewed studies and reports regarding sustainability practiced by AI. The analysis found that there are four different domains of AI applications in sustainability in Mega Sporting Events related to "Energy Efficiency by Predictive Controlling," "Mobility & Crowd Flow Optimization," "Circular Procurement & Waste Management," and "Risk Resilience by Predictive Maintenance." The results show that AI can make significant contributions towards reducing overall "Energy Consumption," "Congestions," and "Generation of Waste," as well as increasing "Resilience." Nevertheless, these should be properly implemented by "Clear Governance & Collaborative Stakeholder Engagements." Based on these investigations, it is suggested that there should be an "AI-for-Sustainability" (AI4S) framework used in future Mega Sporting Event planning.