AI-Driven Carbon-Neutral Computing Sustainability: A Data-Driven Framework Integrating Machine Learning and Environmental–Economic Systems

Mei Bie, Siyu Chen, Yongli Wang, Kai Song

While artificial intelligence (AI) can improve energy efficiency in carbon neutrality applications, its high energy consumption and rebound effect weaken the actual emission reduction effect. To address the issues of high energy consumption and the rebound effect of AI weakening emission reduction, this paper proposes a green AI-driven environmental economic computing framework. First, an energy consumption perception index is introduced, and carbon emissions are monitored in real time using Carbontracker version 2.4.2. Second, multi-task learning is used to predict energy demand and emissions based on multi-source data. Third, the rebound effect is quantified and corrected using an elasticity coefficient model. Finally, resource allocation is optimized under environmental constraints through reinforcement learning, and a closed-loop feedback mechanism is constructed. Experimental results show that the carbon emissions from GPT-3 training are as high as 590 kgCO2, while the emissions from YOLOv5 are only 59 kgCO2. Dynamic batch processing improves energy efficiency by 45%, and the knowledge distillation rebound index is 0.75, but the net energy-saving rate is only 9.1%. The information technology industry achieved a synergy index of 0.88 through AI optimization, but the response time of dedicated hardware is 1.5 s (three times faster than the cloud), indicating that large-scale models have high energy consumption and need optimization to prevent rebound. Real-time feedback and hardware scheduling are key to achieving carbon neutrality.