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Since 2021, China has deployed more than 2.1 million 5G base stations toincrease the network capacity and provide ubiquitous digital connectivity formobile terminals. However, the launch of 5G networks also exacerbates themisalignment between cellular traffic and energy consumption, which reducescarbon efficiency - the amount of network traffic that can be delivered foreach unit of carbon emission. In this study, we develop a large-scaledata-driven framework to estimate the carbon emissions induced by mobilenetworks. We show that the decline in carbon efficiency leads to a carbonefficiency trap, estimated to cause additional carbon emissions of 23.82 +-1.07 megatons in China. To mitigate the misalignment and improve energyefficiency, we propose DeepEnergy, an energy-saving method leveragingcollaborative deep reinforcement learning and graph neural networks. DeepEnergymodels complex collaboration among cells, making it possible to effectivelycoordinate the working state of tens of thousands of cells, which could helpover 71% of Chinese provinces avoid carbon efficiency traps. In addition,applying DeepEnergy is estimated to reduce 20.90 +- 0.98 megatons of carbonemissions at the national level in 2023. We further assess the effects ofadopting renewable energy and discover that the mobile network could accomplishmore than 50% of its net-zero goal by integrating DeepEnergy and solar energysystems. Our study provides insight into carbon emission mitigation in 5Gnetwork infrastructure launching in China and overworld, paving the way towardsachieving sustainable development goals and future net-zero mobile networks.

Carbon emissions and sustainability of launching 5G mobile networks in China