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Carbon emissions of 5G mobile networks in China

Nature Sustainability volume 6, pages 1620–1631 (2023)Cite this article

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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.

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Fig. 1: Analysis of carbon efficiency after the launch of 5G networks.
Fig. 2: Cellular traffic and energy consumption show huge spatiotemporal misalignment.
Fig. 3: Performance analysis of energy-saving methods.
Fig. 4: Renewable energy helps achieve net-zero mobile networks.

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Data availability

The numbers of base stations and mobile users in each province are listed in Supplementary Tables 3 and 4. The network traffic data and the number of mobile users in Nanchang are listed in Supplementary Table 2. Source data are provided with this paper.

Code availability

The code used in this study can be downloaded from https://github.com/Tong89/Sustainability_5G.

References

  1. Manyika, J. & Roxburgh, C. The Great Transformer: The Impact of the Internet on Economic Growth and Prosperity (McKinsey Global Institute, 2011).

  2. Dang, S., Amin, O., Shihada, B. & Alouini, M.-S. What should 6G be? Nat. Electron. 3, 20–29 (2020).

    Article  Google Scholar 

  3. Mwangama, J., Malila, B., Douglas, T. & Rangaka, M. What can 5G do for healthcare in Africa? Nat. Electron. 3, 7–9 (2020).

    Article  Google Scholar 

  4. Gohar, A. & Nencioni, G. The role of 5G technologies in a smart city: the case for intelligent transportation system. Sustainability 13, 5188 (2021).

    Article  Google Scholar 

  5. Dai, C., Liu, X., Lai, J., Li, P. & Chao, H.-C. Human behavior deep recognition architecture for smart city applications in the 5G environment. IEEE Netw. 33, 206–211 (2019).

    Article  Google Scholar 

  6. Taleb, T., Afolabi, I. & Bagaa, M. Orchestrating 5G network slices to support industrial internet and to shape next-generation smart factories. IEEE Netw. 33, 146–154 (2019).

    Article  Google Scholar 

  7. Al-Bawri, S. S., Islam, M. T., Islam, M. S., Singh, M. J. & Alsaif, H. Massive metamaterial system-loaded MIMO antenna array for 5G base stations. Sci. Rep. 12, 14311 (2022).

    Article  CAS  Google Scholar 

  8. Hecht, J. et al. The bandwidth bottleneck. Nature 536, 139–142 (2016).

    Article  CAS  Google Scholar 

  9. I, C.-L., Han, S. & Bian, S. Energy-efficient 5G for a greener future. Nat. Electron. 3, 182–184 (2020).

    Article  Google Scholar 

  10. Ding, Y. et al. Carbon emissions and mitigation potentials of 5G base station in China. Resour. Conserv. Recycl. 182, 106339 (2022).

    Article  CAS  Google Scholar 

  11. Tomás, J. P. China Ends August with 2.1 Million 5G Base Stations: Report (RCR Wireless News, 2022); https://www.rcrwireless.com/20220923/5G/china-ends-august-2-million-5G-base-stations-report

  12. Griggs, D. et al. Sustainable development goals for people and planet. Nature 495, 305–307 (2013).

    Article  CAS  Google Scholar 

  13. Guo, J., Wang, L., Zhou, W. & Wei, C. Powering green digitalization: evidence from 5G network infrastructure in China. Resour. Conserv. Recycl. 182, 106286 (2022).

    Article  Google Scholar 

  14. Ilieva, R. T. & McPhearson, T. Social-media data for urban sustainability. Nat. Sustain. 1, 553–565 (2018).

    Article  Google Scholar 

  15. Yang, C., Li, J., Ni, Q., Anpalagan, A. & Guizani, M. Interference-aware energy efficiency maximization in 5G ultra-dense networks. IEEE Trans. Commun. 65, 728–739 (2016).

    Article  Google Scholar 

  16. Birdsey, R. A. Carbon Storage and Accumulation in United States Forest Ecosystems Vol. 59 (US Department of Agriculture, Forest Service, 1992).

  17. Peng, C., Lee, S.-B., Lu, S., Luo, H. & Li, H. Traffic-driven power saving in operational 3G cellular networks. In Proc. 17th Annual International Conference on Mobile Computing and Networking (eds Ramanathan, P., Nandagopal, T. & Levine, B.) 121–132 (Association for Computing Machinery, 2011).

  18. Rostami, S., Heiska, K., Puchko, O., Leppanen, K. & Valkama, M. Pre-grant signaling for energy-efficient 5G and beyond mobile devices: method and analysis. IEEE Trans. Green Commun. Netw. 3, 418–432 (2019).

    Article  Google Scholar 

  19. Wu, J., Zhang, Y., Zukerman, M. & Yung, E. K.-N. Energy-efficient base-stations sleep-mode techniques in green cellular networks: a survey. IEEE Commun. Surv. Tutor. 17, 803–826 (2015).

    Article  Google Scholar 

  20. Yu, G., Chen, Q. & Yin, R. Dual-threshold sleep mode control scheme for small cells. IET Commun. 8, 2008–2016 (2014).

    Article  Google Scholar 

  21. Chamola, V. & Sikdar, B. Solar powered cellular base stations: current scenario, issues and proposed solutions. IEEE Commun. Mag. 54, 108–114 (2016).

    Article  Google Scholar 

  22. Freeman, J. M. et al. System Advisor Model (SAM) General Description (National Renewable Energy Lab, 2018).

  23. Waldman, J., Sharma, S., Afshari, S. & Fekete, B. Solar-power replacement as a solution for hydropower foregone in US dam removals. Nat. Sustain. 2, 872–878 (2019).

    Article  Google Scholar 

  24. Peng, W. et al. Managing China’s coal power plants to address multiple environmental objectives. Nat. Sustain. 1, 693–701 (2018).

    Article  Google Scholar 

  25. Wang, J. et al. Exploring the trade-offs between electric heating policy and carbon mitigation in China. Nat. Commun. 11, 6054 (2020).

    Article  CAS  Google Scholar 

  26. Bogdanov, D. et al. Radical transformation pathway towards sustainable electricity via evolutionary steps. Nat. Commun. 10, 1077 (2019).

    Article  Google Scholar 

  27. Statistics in Communication Industry (People’s Republic of China’s Ministry of Industry and Information Technology, 2022); https://www.miit.gov.cn/gxsj/tjfx/txy/index.html

  28. Arnold, O., Richter, F., Fettweis, G. & Blume, O. Power consumption modeling of different base station types in heterogeneous cellular networks. In 2010 Future Network & Mobile Summit (ed. Saracco, R.) 1–8 (IEEE, 2010).

  29. I, C.-L. et al. Toward green and soft: a 5G perspective. IEEE Commun. Mag. 52, 66–73 (2014).

    Article  Google Scholar 

  30. Crawley, D. B. et al. Energyplus: creating a new-generation building energy simulation program. Energy Build. 33, 319–331 (2001).

    Article  Google Scholar 

  31. Notice of the Office of the People’s Government of Nanchang City on Forwarding the 2021 Plan of the Municipal Development and Reform Commission and State Grid Nanchang Power Supply Company for Orderly Power Consumption of Nanchang Power Grid (Nanchang Municipal People’s Government, 2021); http://www.nc.gov.cn/nc_xxgk/jsp/zfgb/ncgb_content.jsp?mid=a731211fe1094abfaea8ab348bd5ba8b

  32. China Electricity Council Annual Development Report of China’s Power Industry (2021) 1st edn (China Building Materials Press, 2021).

  33. Liu, Z. et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524, 335–338 (2015).

    Article  CAS  Google Scholar 

  34. Yang, Y. et al. Mean field multi-agent reinforcement learning. In International Conference on Machine Learning (eds Dy, J. G. & Krause, A.) 5571–5580 (PMLR, 2018).

  35. Yan, J., Yang, Y., Elia Campana, P. & He, J. City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China. Nat. Energy 4, 709–717 (2019).

    Article  CAS  Google Scholar 

  36. Kaabeche, A., Belhamel, M. & Ibtiouen, R. Techno-economic valuation and optimization of integrated photovoltaic/wind energy conversion system. Sol. Energy 85, 2407–2420 (2011).

    Article  Google Scholar 

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Acknowledgements

This research has been supported in part by the National Key Research and Development Program of China under grant no. 2020YFA0711403 to D.J., by the National Natural Science Foundation of China under grant no. U21B2036 to T.J., by the National Natural Science Foundation of China under grant no. 61971267 to Y.L., and the International Postdoctoral Exchange Fellowship Program (Talent Introduction Program) under Project YJ20210274 to T.L. The individuals or organizations who provided funding had no role in study design, data collection, analysis, publication decision or preparation of the paper. We also thank the China Mobile Research Institute for Jiutian platform support and the China Mobile Group Jiangxi Company for data support.

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Authors and Affiliations

  1. Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, China

    Tong Li, Yibo Ma, Wenzhen Huang, Depeng Jin & Yong Li

  2. China Mobile Research Institute, Beijing, China

    Li Yu & Yan Zhou

  3. National Digital Switching System Engineering and Technological Research Center, Zhengzhou, China

    Tong Duan

  4. Research Center of 6G Mobile Communications, Huazhong University of Science and Technology, Wuhan, China

    Tao Jiang

Authors
  1. Tong Li
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Contributions

T.L., D.J., Y.L. and T.J. conceived and designed the study. L.Y. and Y.Z. collected and provided the data. T.L., Y.M., T.D. and W.H. carried out the simulations and analyses. All authors contributed to the discussions on the methods and the writing of this article.

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Correspondence to Yong Li or Tao Jiang.

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The authors declare no competing interests.

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Nature Sustainability thanks Dusit Niyato and Jing Meng for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Notes 0–6, Figs. 1–23 and Tables 1–32.

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Source data

Source Data Fig. 1

Source data for Fig. 1.

Source Data Fig. 2

Source data for Fig. 2.

Source Data Fig. 3

Source data for Fig. 3.

Source Data Fig. 4

Source data for Fig. 4.

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Cite this article

Li, T., Yu, L., Ma, Y. et al. Carbon emissions of 5G mobile networks in China. Nat Sustain 6, 1620–1631 (2023). https://doi.org/10.1038/s41893-023-01206-5

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