  A global effort has been initiated to reduce the worldwide greenhouse gas
(GHG) emissions, primarily carbon emissions, by half by 2030 and reach net-zero
by 2050. The development of 6G must also be compliant with this goal.
Unfortunately, developing a sustainable and net-zero emission systems to meet
the users' fast growing demands on mobile services, especially smart services
and applications, may be much more challenging than expected. Particularly,
despite the energy efficiency improvement in both hardware and software
designs, the overall energy consumption and carbon emission of mobile networks
are still increasing at a tremendous speed. The growing penetration of
resource-demanding AI algorithms and solutions further exacerbate this
challenge. In this article, we identify the major emission sources and
introduce an evaluation framework for analyzing the lifecycle of network AI
implementations. A novel joint dynamic energy trading and task allocation
optimization framework, called DETA, has been introduced to reduce the overall
carbon emissions. We consider a federated edge intelligence-based network AI
system as a case study to verify the effectiveness of our proposed solution.
Experimental results based on a hardware prototype suggest that our proposed
solution can reduce carbon emissions of network AI systems by up to 74.9%.
Finally, open problems and future directions are discussed.
