Carbon-Aware Task Offloading Framework for Sustainable Mobile Edge Computing in 6G Environments

This paper presents a carbon-aware task offloading framework designed to enhance sustainability in mobile edge computing environments anticipated in future 6 G networks. The proposed model integrates real-time carbon intensity signals from distributed edge nodes into the offloading decision process, enabling a dynamic balance between environmental impact, system performance and mobile device energy usage. A multi-objective decision engine is developed to jointly minimize carbon emissions and computational delay while maintaining the service quality required by latency-sensitive mobile applications. The framework is evaluated using a simulated 6G-ready MEC architecture with time-varying grid carbon profiles, heterogeneous workloads and mobile energy constraints. Experimental analysis demonstrates that the proposed approach reduces total carbon emissions by up to 58 percent compared to latency-optimized baselines and by 42 percent compared to conventional energy-oriented strategies. The method achieves a 92 percent task completion ratio under deadlines, with only an approximate 7 percent increase in average latency relative to the lowest-latency configuration. Mobile device energy consumption remains comparable to existing energy-focused models, confirming the feasibility of carbon-aware optimization without sacrificing user experience. These results highlight the potential of environmentally responsible task management as a core component of future sustainable mobile computing systems.