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Artificial Intelligence

A basic guide to AI.

Energy Usage & Carbon Dioxide Emissions

Artificial Intelligence development and usage results in a massive use of energy and in increased CO2 emissions.

Fossil Fuel Extraction

  • Training and running AI models, particularly large language models, requires enormous amounts of energy, often derived from fossil fuels. This contributes to greenhouse gas emissions and climate change.

Carbon Footprint

  • Training can produce about 626,000 pounds of carbon dioxide- the equivalent of 300 round-trip flights between New York and San Francisco, or nearly 5 times the lifetime emissions of the average car. 
  • Researchers estimated that creating GPT-3 consumed 1,287 megawatt hours of electricity and generated 552 tons of carbon dioxide equivalent- the equivalent of 123 gasoline-powered passenger vehicles driven for one year.

E-Waste

  • The production and improper disposal of AI hardware generates electronic waste, which contains harmful chemicals that can contaminate the environment.

Lack of Transparency

There is a general lack of transparency and accountability about the environmental impact of AI development and continuing usage.

  • For ChatGPT’s latest model, GPT4, [OpenAI] hasn’t said anything about either how long it’s been trained, where it’s trained, or anything at all about the data they’re using...So essentially, it means it’s impossible to estimate emissions.” Dr. Sasha Luccioni, AI Ethics Researcher.

  • Newer AI models are getting bigger – and more energy intensive. Bigger models require the use of more and more powerful graphics processing units (GPUs), and take longer to train – using up more resources and energy.

  • Errors in artificial intelligence algorithms and decision-making processes lead to environmental injustice and inequality.

  • AI technologies may disrupt natural ecosystems, jeopardizing wildlife habitats and migration patterns.

  • One non-peer-reviewed study, led by researchers at UC Riverside, estimates that training GPT3 in Microsoft’s state-of-the-art US data centers could potentially have consumed 700,000 liters (184,920.45 gallons) of freshwater. In the absence of accurate, public data, the researchers had to assume the “water use effectiveness”, or the ratio of energy a data center uses and the water used to keep it cooled and functioning, based on Microsoft’s self-reported average.

  • By 2040 it is expected that the emissions from the Information and Communications Technology industry as a whole will reach 14% of the global emissions.

Li, P., Yang, J., Islam, M. A., & Ren, S. (2023). Making AI less "thirsty": Uncovering and addressing the secret water footprint of AI models. (). Ithaca: Cornell University Library, arXiv.org. https://doi.org/10.48550/arxiv.2304.03271

Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. (). Ithaca: Cornell University Library, arXiv.org. https://doi.org/10.48550/arxiv.1906.02243

Zhuk A. Artificial Intelligence Impact on the Environment: Hidden Ecological Costs and Ethical-Legal Issues. Journal of Digital Technologies and Law. 2023;1(4):932-954. https://doi.org/10.21202/jdtl.2023.40