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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

邮发代号 80-973

2018 Impact Factor: 3.883

Frontiers of Environmental Science & Engineering  2024, Vol. 18 Issue (12): 158   https://doi.org/10.1007/s11783-024-1918-y
  本期目录
Revisit the environmental impact of artificial intelligence: the overlooked carbon emission source?
Yang Yu1, Jiahui Wang1, Yu Liu2, Pingfeng Yu1,3,4, Dongsheng Wang1,3,4, Ping Zheng1, Meng Zhang1,3,4()
1. Department of Environmental Engineering, College of Environmental & Resource Sciences, Zhejiang University, Hangzhou 310058, China
2. Engineering Laboratory of Low-Carbon Unconventional Water Resources Utilization and Water Quality Assurance, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
3. Zhejiang Province Key Laboratory for Water Pollution Control and Environmental Safety, Hangzhou 310058, China
4. Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan 314100, China
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Abstract

The boosting development of artificial intelligence (AI) is contributing to rapid exponential surge of computing power demand, which results in the concerns on the increased energy consumption and carbon emission. To highlight the environmental impact of AI, a quantified analysis on the carbon emission associated with AI systems was conducted in this study, with the hope of offering guidelines for police maker to setup emission limits or studies interested in this issue and beyond. It has been discovered that both industry and academia play pivotal roles in driving AI development forward. The carbon emissions from 79 prominent AI systems released between 2020 and 2024 were quantified. The projected total carbon footprint from the AI systems in the top 20 of carbon emissions could reach up to 102.6 Mt of CO2 equivalent per year. This could potentially have a substantial impact on the environmental market, exceeding $10 billion annually, especially considering potential carbon penalties in the near future. Hence, it is appealed to take proactive measures to develop quantitative analysis methodologies and establish appropriate standards for measuring carbon emissions associated with AI systems. Emission cap is also crucial to drive the industry to adopt more environmentally friendly practices and technologies, in order to build a more sustainable future for AI.

Key wordsCarbon emission    Artificial intelligence (AI)    Training compute    Energy consumption    Economic analysis    Emission caps
收稿日期: 2024-06-30      出版日期: 2024-10-18
Corresponding Author(s): Meng Zhang   
 引用本文:   
. [J]. Frontiers of Environmental Science & Engineering, 2024, 18(12): 158.
Yang Yu, Jiahui Wang, Yu Liu, Pingfeng Yu, Dongsheng Wang, Ping Zheng, Meng Zhang. Revisit the environmental impact of artificial intelligence: the overlooked carbon emission source?. Front. Environ. Sci. Eng., 2024, 18(12): 158.
 链接本文:  
https://academic.hep.com.cn/fese/CN/10.1007/s11783-024-1918-y
https://academic.hep.com.cn/fese/CN/Y2024/V18/I12/158
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1 J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, F L Aleman, D Almeida, J Altenschmidt, S Altman, S J Anadkat, et al. (2023). Gpt-4 technical report. arXiv: abs/2303.08774.
2 M F Argerich, M Patiño-Martínez. (2024). Measuring and improving the energy efficiency of large language models inference. IEEE Access: Practical Innovations, Open Solutions, 12: 80194–80207
https://doi.org/10.1109/ACCESS.2024.3409745
3 Bozeman J F (2024) Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity. Frontiers of Environmental Science & Engineering, 18(5): 65
4 T B Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, et al. (2020). Language Models are Few-Shot Learners. ArXiv: abs/2005.14165
5 W Chen, Q Zhang, L Hu, Y Geng, C Liu. (2023). Understanding the greenhouse gas emissions from China’s wastewater treatment plants: based on life cycle assessment coupled with statistical data. Ecotoxicology and Environmental Safety, 259: 115007
https://doi.org/10.1016/j.ecoenv.2023.115007
6 A A Chien, L Lin, H Nguyen, V Rao, T Sharma, R Wijayawardana (2023). Reducing the carbon impact of generative AI inference (today and in 2035). In: Proceedings of the 2nd Workshop on Sustainable Computer Systems, Association for Computing Machinery, New York, USA, Article 11, 1–7
7 A de Vries. (2023). The growing energy footprint of artificial intelligence. Joule, 7(10): 2191–2194
https://doi.org/10.1016/j.joule.2023.09.004
8 Energy Institute (2024). Statistical Review of World Energy. Edinburgh. Heriot-Watt University Available online at the website of energyinst.org (accessed Augest 02, 2024)
9 AI Epoch (2024). Compute Trends Across Three Eras of Machine Learning. San Francisco. Rethink Priorities. Available online at the website of epochai.org/blog/compute-trends (accessed April 24, 2024)
10 R Hannah, R Pable, R Max (2020). Greenhouse gas emissions. Our World in Data. Available online at the website of ourworldindata.org/greenhouse-gas-emissions (accessed Augest 02, 2024)
11 H HuangR MaH (2024) Ren. Scientific and technological innovations of wastewater treatment in China. Frontiers of Environmental Science & Engineering 18(6): 72
12 A Lacoste, A S Luccioni, V Schmidt, T Dandres (2019). Quantifying the carbon emissions of machine learning. ArXiv: abs/1910.09700.
13 A Luers, J Koomey, E Masanet, O Gaffney, F Creutzig, J Lavista Ferres, E Horvitz (2024). Will AI accelerate or delay the race to net-zero emissions? Nature, 628(8009): 718–720
14 OWID (2024). Per capita CO2 emissions. Oxfordshire. Global Change Data Lab. Available online at the website of ourworldindata.org/grapher/co-emissions-per-capita (accessed April 25, 2024)
15 M C Rillig, M Ågerstrand, M Bi, K A Gould, U Sauerland. (2023). Risks and benefits of large language models for the environment. Environmental Science & Technology, 57(9): 3464–3466
https://doi.org/10.1021/acs.est.3c01106
16 H Scells, S Zhuang, G Zuccon (2022). Reduce, reuse, recycle. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, New York, NY, USA, 2825–2837
17 J Sevilla, L Heim, A C Ho, T Besiroglu, M Hobbhahn, P Villalobos (2022). Compute Trends Across Three Eras of Machine Learning. International Joint Conference on Neural Networks (IJCNN), 1–8
18 Similarweb(2024). Website Analysis. New York. Similarweb Ltd. Available online at the website of pro.similarweb.com/#/digitalsuite (accessed August 27, 2024).
19 WB (20232023. State and Trends of Carbon Pricing 2023. Washington, D.C. World Bank. Available online at the website of hdl.handle.net/10986/39796 (accessed April 25, 2024)
20 J J Zhu, J Jiang, M Yang, Z J Ren. (2023). ChatGPT and environmental research. Environmental Science & Technology, 57(46): 17667–17670
https://doi.org/10.1021/acs.est.3c01818
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