Artificial intelligence (AI) has revolutionized various sectors, offering solutions to complex problems and enhancing efficiency. However, its rapid expansion has raised concerns about its environmental footprint. As AI systems require vast amounts of energy for data processing and training, the increasing demand for these technologies significantly contribute to carbon emissions and resource depletion, according to npr.org.
AI operates through algorithms that process vast amounts of data to recognize patterns, make predictions, or perform tasks. These algorithms, often modeled on the structure of human neural networks, requiring extensive computational resources for development and deployment. Training large AI models involves processing terabytes of data across many servers, which requires significant electricity. This energy usage translates into substantial environmental impacts. For example, training a single large language model can produce an estimated 539 tons of carbon dioxide, comparable to the annual electricity consumption of over 100 average US homes. Such environmental considerations are becoming an important aspect of AI research and application, according to npr.org.
AI’s operational phase intensifies energy demands. A single query to an AI-powered chatbot, such as Chat Generative Pre-Trained Transformer (Chat GPT), a language model designed to generate human-like responses based on user prompts, can use up to ten times as much energy as a standard Google search, translating into increased carbon emissions and water use, according to washingtonpost.com. These demands also strain electricity grids, which are already under pressure from climate change effects like extreme weather, increased energy needs for cooling during heat waves, and disruptions to power infrastructure, according to npr.org.
“One query to ChatGPT uses approximately as much electricity as could light one light bulb for about 20 minutes,” researcher Jesse Dodge said, according to npr.org. “So, you can imagine with millions of people using something like that every day, that adds up to a really large amount of electricity.”
Addressing AI’s environmental impact necessitates a multifaceted approach. Implementing sustainable AI practices, such as optimizing algorithms for energy efficiency and utilizing renewable energy sources for data centers, can mitigate negative effects. Furthermore, enhancing transparency regarding AI’s energy consumption and environmental costs is crucial for informed decision-making and policy development. In fact, using machine learning algorithms could improve the Environmental Protection Agency’s ability to detect water pollution violators by more than 600 percent compared to relying on random facility inspections, conveying how this form of technology serves a useful purpose in that sector, according to enviornment.upenn.edu.
Featured Image by Sofia Latrille ’25
