Unique Presentation Identifier:
V13
Program Type
Graduate
Faculty Advisor
Dr. Robin Ghosh
Document Type
Poster
Location
Online
Start Date
29-4-2025 8:00 AM
Abstract
Artificial Intelligence (AI) has rapidly transformed industries and daily life, but beneath its advancements lies a growing environmental concern. This paper explores the often-overlooked ecological footprint of AI technologies, focusing on real-world data and tangible impacts. Notably, training OpenAI’s GPT-3 model alone consumed approximately 1,287 megawatt-hours (MWh) of electricity—equivalent to the annual consumption of over 120 U.S. homes—and emitted 500 metric tons of CO₂, comparable to driving 112 gasoline-powered cars for a year.
Furthermore, the environmental cost extends beyond electricity consumption. Data centers powering AI require vast amounts of water for cooling, contributing to local water shortages—highlighted by recent legislation in Virginia mandating water usage transparency. The demand for specialized hardware (GPUs, TPUs) accelerates mining activities in regions such as Africa and South America, exacerbating deforestation, pollution, and labor exploitation. Additionally, AI-driven consumerism, such as fast fashion brands like Shein, intensifies overproduction and waste, while hardware turnover contributes to millions of tons of e-waste projected by 2030.
In response to these challenges, the emergence of the Green AI movement presents a pathway to sustainable AI development. Companies like Google and Microsoft have committed to powering their data centers with 100% renewable energy by 2030, while Hugging Face promotes the reuse of pre-trained models and the development of efficient, low-energy AI systems. Researchers at the Allen Institute advocate for transparent reporting of energy usage, ensuring AI models are evaluated not only by accuracy but also by environmental cost. Governments are introducing regulations encouraging green practices, alongside efforts to develop recyclable AI hardware.
By highlighting real-world data and the rising adoption of Green AI practices, this study calls for urgent collective action—ensuring that AI continues to advance while aligning with global climate goals and resource sustainability.
Recommended Citation
Tamut, Hayin, "The Hidden Cost of Intelligence: Environmental Impacts and the Rise of Green AI Solutions" (2025). ATU Student Research Symposium. 24.
https://orc.library.atu.edu/atu_rs/2025/2025/24
Included in
The Hidden Cost of Intelligence: Environmental Impacts and the Rise of Green AI Solutions
Online
Artificial Intelligence (AI) has rapidly transformed industries and daily life, but beneath its advancements lies a growing environmental concern. This paper explores the often-overlooked ecological footprint of AI technologies, focusing on real-world data and tangible impacts. Notably, training OpenAI’s GPT-3 model alone consumed approximately 1,287 megawatt-hours (MWh) of electricity—equivalent to the annual consumption of over 120 U.S. homes—and emitted 500 metric tons of CO₂, comparable to driving 112 gasoline-powered cars for a year.
Furthermore, the environmental cost extends beyond electricity consumption. Data centers powering AI require vast amounts of water for cooling, contributing to local water shortages—highlighted by recent legislation in Virginia mandating water usage transparency. The demand for specialized hardware (GPUs, TPUs) accelerates mining activities in regions such as Africa and South America, exacerbating deforestation, pollution, and labor exploitation. Additionally, AI-driven consumerism, such as fast fashion brands like Shein, intensifies overproduction and waste, while hardware turnover contributes to millions of tons of e-waste projected by 2030.
In response to these challenges, the emergence of the Green AI movement presents a pathway to sustainable AI development. Companies like Google and Microsoft have committed to powering their data centers with 100% renewable energy by 2030, while Hugging Face promotes the reuse of pre-trained models and the development of efficient, low-energy AI systems. Researchers at the Allen Institute advocate for transparent reporting of energy usage, ensuring AI models are evaluated not only by accuracy but also by environmental cost. Governments are introducing regulations encouraging green practices, alongside efforts to develop recyclable AI hardware.
By highlighting real-world data and the rising adoption of Green AI practices, this study calls for urgent collective action—ensuring that AI continues to advance while aligning with global climate goals and resource sustainability.