As the global climate crisis intensifies, governments, industries, and communities worldwide face an increasing pressure to accelerate efforts toward achieving Net Zero emissions. Although the rapid adoption of Artificial Intelligence (AI) is transforming the Information and Communications Technology (ICT) landscape, it presents both opportunities and challenges. While AI offers powerful tools to optimize energy use, enhance supply chain efficiency, and accelerate renewable energy adoption, its computational demands—particularly during model training and deployment—pose significant environmental challenges, such as increased energy consumption and carbon emissions. This paper examines the dual impact of AI on Net Zero goals by exploring two key concepts: “AI for Sustainability” and “Sustainable AI.”

To mitigate AI’s environmental impact across the ICT value chain, this paper underscores the importance of evaluating the entire lifecycle of AI systems, from inception to retirement. It outlines strategies to reduce embodied emissions in AI infrastructure, transition to renewable energy sources, and minimize the energy consumption of Machine Learning (ML) processes. Additionally, it highlights how AI can enhance energy efficiency across key areas, including Radio Access Networks (RAN), core networks, User Equipment (UE), and data centers. The critical role of Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) in assessing the effectiveness of these strategies is also emphasized.

AI-driven techniques are emerging as essential tools to optimize energy and sustainability performance in the rapidly evolving telecommunications sector. This paper explores forward-looking aspects such as the integration of AI with quantum computing, Non-Terrestrial Networks (NTNs), and opportunities for AI to enhance spectral efficiency and enable intent-based automation in networks.

Summarizing the challenges of managing energy consumption, carbon emissions, water usage, and electronic waste, this paper provides actionable recommendations for leveraging AI to analyze radio network conditions and user preferences to enhance sustainability performance while maintaining Quality of Service (QoS). While AI holds immense potential to optimize the environmental sustainability of next-generation networks, the paper underscores the critical need to address its broader social and environmental impacts. We invite you to explore this comprehensive analysis and discover how thoughtful implementation of AI can drive meaningful progress toward a sustainable future.