Sources
This page lists external research, institutional reports, and scientific publications that support claims made in the poster. Each numbered item can be referenced as [1], [2], etc.
Note: This is an index, not an authority. Where possible, links go to primary sources (official reports, journals, or archives).
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International Energy Agency (IEA). (2024). Data Centres and Data Transmission Networks.
https://www.iea.org/reports/data-centres-and-data-transmission-networks
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Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning. ACL.
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Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020). Recalibrating global data center energy-use estimates. Science, 367(6481), 984–986.
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United Nations Environment Programme (UNEP). (2023). Artificial Intelligence and the Environment: Opportunities and Challenges.
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Li, P., Yang, J., Islam, M. A., & Ren, S. (2023). Making AI Less Thirsty: Uncovering and Addressing the Secret Water Footprint of AI Models.
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Forti, V., Baldé, C. P., Kuehr, R., & Bel, G. (2020). The Global E-waste Monitor. United Nations University.
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Rolnick, D., et al. (2019). Tackling Climate Change with Machine Learning. Nature Climate Change, 9, 518–524.
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International Energy Agency (IEA). (2023). Digitalization and Energy Efficiency.
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World Wildlife Fund (WWF). (2020). Artificial Intelligence for Conservation.
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Food and Agriculture Organization of the United Nations (FAO). (2022). Artificial Intelligence in Agriculture.
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MIT Climate Portal. (2024). AI and Climate Change.
Mapping note
The poster uses these sources as follows (examples): electricity use [1][3], training energy [2], water footprint [5], e-waste [6], climate applications [7][11], efficiency [8], conservation [9], agriculture [10].