Carbon emissions of 5G mobile networks in China

Tong Li, Li Yu, Yibo Ma, Tong Duan, Wenzhen Huang, Yan Zhou, Depeng Jin, Yong Li () and Tao Jiang ()
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Tong Li: Tsinghua University
Li Yu: China Mobile Research Institute
Yibo Ma: Tsinghua University
Tong Duan: National Digital Switching System Engineering and Technological Research Center
Wenzhen Huang: Tsinghua University
Yan Zhou: China Mobile Research Institute
Depeng Jin: Tsinghua University
Yong Li: Tsinghua University
Tao Jiang: Huazhong University of Science and Technology

Nature Sustainability, 2023, vol. 6, issue 12, 1620-1631

Abstract: Abstract Telecommunication using 5G plays a vital role in our daily lives and the global economy. However, the energy consumption and carbon emissions of 5G mobile networks are concerning. Here we develop a large-scale data-driven framework to quantitatively assess the carbon emissions of 5G mobile networks in China, where over 60% of the global 5G base stations are implemented. We reveal a carbon efficiency trap of 5G mobile networks leading to additional carbon emissions of 23.82 ± 1.07 Mt in China, caused by the spatiotemporal misalignment between cellular traffic and energy consumption in mobile networks. To address this problem, we propose an energy-saving method, called DeepEnergy, leveraging collaborative deep reinforcement learning and graph neural networks, to make it possible to effectively coordinate the working state of 5G cells, which could help over 71% of Chinese provinces avoid carbon efficiency traps. The application of DeepEnergy can potentially reduce carbon emissions by 20.90 ± 0.98 Mt at the national level in 2023. Furthermore, the mobile network in China could accomplish more than 50% of its net-zero goal by integrating DeepEnergy with solar energy systems. Our study deepens the insights into carbon emission mitigation in 5G networks, paving the way towards sustainable and energy-efficient telecommunication infrastructures.

Date: 2023
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DOI: 10.1038/s41893-023-01206-5

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