References

• Amaya, A., Bach, N., Phillips, C., Tejedor, A., Steinhaeuser, K., Lakkaraju, H., & Kammen, D. M. (2021). Social biases in solar geoengineering research. Nature Climate Change, 11(12), 1063-1067. google scholar
• Amodei, D., & Hernandez, D. (2018). AI and Compute. OpenAI Blog. google scholar
• Danish, S. S. (2023). AI-enabled energy policy for a sustainable future. Sustainability, 15, 7643. google scholar
• Dhar, V. (2020). The carbon impact of artificial intelligence. Nature Machine Intelligence, 2, 423-425. https://doi.org/10.1038/s42256-020-0219-9 google scholar
• Dobbe, R., & Whittaker, M. (2019). AI and climate change: How they’re connected, and what we can do about it. Climate Research. google scholar
• Eskom. (2022). Environmental impact. https://www.eskom.co.za/OurCompany/SustainableDevelopment/EnvironmentalImpact/Pages/CDM_ Projects.aspx google scholar
• Eskom. (2022). Load shedding data. http://loadshedding.eskom.co.za/LoadShedding google scholar
• Freeman, K. S. (2023). AI and energy consumption: Are we headed for trouble? Imore News. google scholar
• Gee, G. C., & Payne-Sturges, D. C. (2004). Environmental health disparities: A framework integrating psychosocial and environmental concepts. google scholar
• Environmental Health Perspectives, 112, 1645-1653. https://doi.org/10.1289/ehp.7074 google scholar
• Gichuki, C. (2022). Konza Technopolis eyes AI, manufacturing and agriculture. The Exchange. google scholar
• Hao, K. (2019). Training a single AI model can emit as much carbon as five cars in their lifetimes: Deep learning has a terrible carbon footprint. MIT Technology Review. google scholar
• Holifield, R., Chakraborty, J., & Walker, G. (Eds.). (2017). The Routledge Handbook of Environmental Justice. Routledge. google scholar
• Hutson, M. (2022). Measuring AI’s Carbon Footprint: New tools track and reduce emissions from machine learning. IEEE Spectrum. google scholar
• Jennifer, L. (2023). How big is the CO2 footprint of AI models? ChatGPT’s emissions. google scholar
• Kumari Rigaud, K., de Sherbinin, A., Jones, B., Bergmann, J., Clement, V., Ober, K., Schewe, J., Adamo, S., McCusker, B., Heuser, S., & Midgley, A. (2018). Groundswell: Preparing for Internal Climate Migration. World Bank. google scholar
• Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the carbon emissions of machine learning. Workshop on Tackling Climate Change with Machine Learning. google scholar
• Li, R., Wang, W., Shi, Y., & Wang, P. (2023). Advanced Material Design and Engineering for Water-Based Evaporative Cooling. Advanced Materials, 2209460. https://doi.org/10.1002/adma.202209460 google scholar
• Lu, C. (2017). AI, native supercomputing and the revival of Moore’s Law. APSIPA Transactions on Signal and Information Processing, 6, E9. doi:10.1017/ATSIP.2017.9 google scholar
• Luque-Ayala, A., Chapman, A., Scuriatti, C., Maia, L., Hunter, C., & Peres, W. (2021). Digital territories: Google Maps as a political technique in the reorganization of urban energy systems. Energy Research & Social Science, 79, 102138. https://doi.org/10.1016/j.erss.2021.102138 google scholar
• Minnesota Pollution Control Agency (MPCA). (2022). Environmental justice framework. google scholar
• Mohai, P., Pellow, D., & Roberts, J. T. (2009). Environmental justice. Annual Review of Environment and Resources, 34, 405-430. https://doi.org/10.1146/annurev-environ-082508-094348 google scholar
• Morello-Frosch, R., & Lopez, R. (2006). The riskscape and the color line: Examining the role of segregation in environmental health disparities. Environmental Research, 102(2), 181-196. https://doi.org/10.1016/j.envres.2006.05.007 google scholar
• Nzimande, B. (2022). Minister Blade Nzimande: Abandonment of Kusile units 5 and 6 construction projects. Department of Higher Education, Science and Innovation. google scholar
• Risser, M. D., & Wehner, M. F. (2017). Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey. Geophysical Research Letters, 44(24), 12,457-12,464. https://doi.org/10.1002/2017GL075888 google scholar
• Rolnick, D., Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A.S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A., Maharaj, T., Sherwin, E.D., Mukkavilli, S.K., Kording, K.P., Gomes, C., Ng, A.Y., Hassabis, D., Platt, J.C., ... Bengio, Y. (2021). Tackling Climate Change with Machine Learning. arXiv. google scholar
• Rolnick, D., Philip, L., Kaack, L., Lacoste, A., Luccioni, A. (2019). Trends and applications in climate informatics. Journal of Parallel and Distributed Computing, 134, 141-150. https://doi.org/10.1016/j.jpdc.2019.08.006 google scholar
• Saenko, K. (2022). The huge carbon footprint of AI algorithms: Machine learning has a disastrous environmental impact. Boston Globe. google scholar
• Schwartz, R., Dodge, J., Smith, N.A., & Etzioni, O. (2019). Green AI. arXiv preprint arXiv:1907.10597. google scholar
• Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for modern deep learning research. Thirty-Fourth AAAI Conference on Artificial Intelligence. google scholar
• Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. arXiv. google scholar
• Whyte, K. P. (2018). What do Indigenous knowledges have to offer climate change research? In S. Díaz, J. Settele, E. Brondízio, & H. T. Ngo (Eds.), The IPCC and Indigenous Peoples (pp. 57-59). Intergovernmental Panel on Climate Change. google scholar
• Wylie, S., Jalbert, K., Dosemagen, S., & Ratto, M. (2022). Advancing climate justice with community-based air quality monitoring and machine learning in California’s Imperial County. Geo: Geography and Environment, 9(1), e00107. google scholar
• Zhou, H., Liu, Q., Yan, K., & Du, Y. (2021). Deep learning enhanced solar energy forecasting with AI-driven IoT. Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/9249387 google scholar

Citations