Araştırma Makalesi
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The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability

Yıl 2024, Cilt: 8 Sayı: 1, 51 - 59, 28.06.2024
https://doi.org/10.26650/acin.1431443

Öz

The exponential ascension of artificial intelligence (AI) prompts profound inquiries concerning equitable access to its advantages versus environmental externalities. While trailblazing economies relish AI’s benefits such as economic expansion and technological eminence, the colossal energy required to train and operate AI systems exacts a hefty toll on the environment, disproportionately burdening marginalized nations. This imbalanced paradigm epitomizes disparities of the digital divide, with impoverished nations bearing externalities while lacking access to innovations. This study aims to explore the intricate relationship between AI and environmental sustainability through a qualitative methodology encompassing a literature review and document analysis of industry practices and viewpoints. The findings unveil AI as a double-edged sword, with empirical analyses exposing its striking carbon emissions and resource depletion, which if left unchecked, could impede global decarbonization initiatives. However, AI also demonstrates strong potential for optimizing energy systems, predictive modelling, and advancing climate solutions if conscientiously developed. The study elucidates this conundrum and proposes responsible innovation pathways involving renewable energy adoption, enhanced efficiency, optimized hardware, carbon accounting, transparency, and legislative mindfulness. Integrating climate justice and digital divide perspectives illuminates avenues for steering AI’s trajectory towards environmental stewardship and inclusive accessibility through proactive collaboration across sectors. Ultimately, collective wisdom will determine whether AI ushers in climate justice or injustice.

Kaynakça

  • 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
Yıl 2024, Cilt: 8 Sayı: 1, 51 - 59, 28.06.2024
https://doi.org/10.26650/acin.1431443

Öz

Kaynakça

  • 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
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Ronald Manhibi 0000-0001-6262-0297

Kudzayi Tarisayi 0000-0003-0086-2420

Yayımlanma Tarihi 28 Haziran 2024
Gönderilme Tarihi 5 Şubat 2024
Kabul Tarihi 21 Mayıs 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 1

Kaynak Göster

APA Manhibi, R., & Tarisayi, K. (2024). The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability. Acta Infologica, 8(1), 51-59. https://doi.org/10.26650/acin.1431443
AMA Manhibi R, Tarisayi K. The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability. ACIN. Haziran 2024;8(1):51-59. doi:10.26650/acin.1431443
Chicago Manhibi, Ronald, ve Kudzayi Tarisayi. “The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability”. Acta Infologica 8, sy. 1 (Haziran 2024): 51-59. https://doi.org/10.26650/acin.1431443.
EndNote Manhibi R, Tarisayi K (01 Haziran 2024) The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability. Acta Infologica 8 1 51–59.
IEEE R. Manhibi ve K. Tarisayi, “The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability”, ACIN, c. 8, sy. 1, ss. 51–59, 2024, doi: 10.26650/acin.1431443.
ISNAD Manhibi, Ronald - Tarisayi, Kudzayi. “The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability”. Acta Infologica 8/1 (Haziran 2024), 51-59. https://doi.org/10.26650/acin.1431443.
JAMA Manhibi R, Tarisayi K. The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability. ACIN. 2024;8:51–59.
MLA Manhibi, Ronald ve Kudzayi Tarisayi. “The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability”. Acta Infologica, c. 8, sy. 1, 2024, ss. 51-59, doi:10.26650/acin.1431443.
Vancouver Manhibi R, Tarisayi K. The Precarious Pirouette: Artificial Intelligence and Environmental Sustainability. ACIN. 2024;8(1):51-9.