A Guide to Artificial Intelligence (AI) for Students

A 2019 article by researchers from University of Massachusetts Amherst quantifies the approximate financial and environmental costs of training a variety of AI tools, and offers their recommendations to lessen harmful global impact.  

• 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.
• Quickly growing 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. 
• Influx of carbon-dioxide emissions
  • 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.
• Constant targeted ads increase spending (e-commerce), increase fast fashion production
  • ​​​​​​​increased consumption and waste
    • Hyper-personalization of marketing campaigns to encourage impulse buying
    • Trend prediction and virtual try-on capabilities
    • AI can help monitor supply chains, but doesn't guarantee ethical practices- the pressure to produce quickly can lead to exploitation
• E-waste produced by AI technology
  • The production and improper disposal of AI hardware generate electronic waste, which contains harmful chemicals that can contaminate the environment.
• Lack of transparency and accountability regarding environmental impact
  • ​​​​​​​“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

• Freshwater Usage

  • 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.

• Data Centers energy consumption
  • ​​​​​​​Unfettered growth of Gen-AI with limited regulatory oversight= rising demand of data centers (a temperature-controlled building that houses computing infrastructure such as servers, data storage drivers, and network equipment). 
  • 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.

The International Energy Agency estimates that by 2026 electricity consumption by data centers,

cryptocurrency, and artificial intelligence could reach 4% of annual global energy usage

(roughly equal to the amount of electricity used by the entire country of Japan)

“What is different about generative AI is the power density it requires. Fundamentally, it is just computing, but a generative AI training cluster might consume seven or eight times more energy than a typical computing workload.”  Noman Bashir, Computing and Climate Impact Fellow at MIT Climate and Sustainability Consortium

(Blog) Straying from True Nature: How AI is spreading Misinformation about Penguins (and other conservation issues)

Bashir, N., Donti, P., Cuff, J., Sroka, S., Ilic, M., Sze, V., Delimitrou, C., & Olivetti, E. (2024). The Climate and Sustainability Implications of Generative AI. An MIT Exploration of Generative AI. https://mit-genai.pubpub.org/pub/8ulgrckc/release/2 

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

Liao M, Lan K, Yao Y. Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework. J Ind Ecol. 2022; 26: 164–182. https://doi.org/10.1111/jiec.13214  

Shirvell, B. (2024). Can We Mitigate AI's Environmental Impacts? Yale School of the Environment News. https://environment.yale.edu/news/article/can-we-mitigate-ais-environmental-impacts

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

Zewe, A. (2025). Explained: Generative AI's Environmental Impact. MIT News. https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117 

There are also AI powered tools with the potential to address several environmental challenges such as climate modeling, renewable energy optimization, sustainable agriculture, disaster prediction & response, and conservation efforts.

• Earth Insights
• Biodiversity Research
• Arctic Conservation
• Predicting Weather Disasters- flood management, storm responses
• Tracking Energy misuse/excessive use
• Ocean Cleanup
• Ocean Ledger
• xView2
• Climate Trace
• ClimateAI
• ClimaX
• WattTime
• Bumble Bee Watch
• Rainforest Connection
• Deltares Aquality App
• IBM Sustainability Accelerator
• CodeCarbon
• Blue River Technology
• DeepMind
• Flood Forecasting
• Tierrasphere
• Green Light- using Google AI to reduce traffic emissions
• Wildfire Tracking
• Project Contrails- using AI to help mitigate aviation's climate impact
• Preserve & restore coral reefs- you can help too!  Take 3 minutes to participate in "Calling in our Corals"- a project that utilizes the natural sounds of healthy coral reefs as a potential tool for coral reef restoration & engages the public in monitoring the health of these ecosystems through citizen science
• Intel Rhino Chip
• AI for peatland restoration
• Electricity Grid Management, identifying opportunities for increased efficiency
• Satellite monitoring to track global climate change impacts and progress

TED talk by Dr. Sasha Luccioni, AI Ethics Researcher    

10/2023