AI-Based Carbon Footprint Assessment of 6G Networks: A Digital Twin Use Case

Global warming poses a severe threat to our planet and significant efforts are being made to reduce carbon emissions. However, technological advancements continue to evolve, driven by the increasing demands of modern civilization and geopolitical competition between the East (e.g., China, Russia, and North Korea) and the West (e.g., Europe and North America). One such transformative technology is 6G communications, which is expected to enable groundbreaking applications such as smart networks and systems, digital twins, holographic communication, integrated sensing and positioning, and heavy-duty autonomous robots. Since 6G infrastructure remains largely theoretical, validating its energy efficiency claims is challenging. Therefore, we use Large Language Models (LLMs) and AI-driven simulations to estimate the carbon footprint of 6G-enabled applications. This work contributes to the ongoing debate on sustainable 6G deployment and offers insights into balancing technological advancement with environmental responsibility. The unit of kWh is employed to express energy consumption, rather than kg CO2 emissions. This methodological choice reflects the variable nature of electricity generation sources, including the growing contribution of renewable energy, thereby providing a more general and less context-specific metric.