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Sustainability and environmental concerns

In this article, Dr Rebecca Lindner considers implications of GenAI for environmental sustainability as well as opportunities for action in education.

In the previous step, you considered the challenges and possibilities generative AI poses for equitable and fair access and inclusion. In this step, you will learn more about some of the issues and implications that generative AI brings for global environmental sustainability as well as the opportunities for environmental education and action in the higher education context.

In the higher education context, debates about the use of generative AI have largely focused on issues of academic integrity, authorship, the future of research and teaching, and shifting careerscapes for our graduates [1]. But, as Step 1.14 and Step 1.15 indicate, there have also been significant reservations voiced about some of the social, political and ethical consequences of our growing reliance on AI technologies as well as the need for informed and thoughtful approaches to addressing these consequences.

In all the excitement that has attended the potential and popularity of generative AI, the environmental risks and opportunities have to date received somewhat less attention [2, 3], even against the backdrop of our worsening global climate crisis.

Environmental risks

In particular, the development and use of large language models such as ChatGPT are incredibly energy intensive. Researchers point to the powerful computing resources that are required for the training and operation of these tools, including the maintenance of data centres with increasingly powerful servers to run massive datasets, as well as the huge amounts of water needed for cooling systems to prevent overheating, carbon dioxide emissions, possible soil pollution and the proliferation of e-waste products [2, 4]. Indeed, it’s estimated that this data centre industry is currently responsible for up to 3% of global greenhouse gas emissions – roughly the same as the airline industry – with the added caveat that the volume of data across the world continues to grow exponentially [2].

Large data center and server room hallway

Given that digital technologies are integral to our daily lives and that generative AI tools are becoming increasingly ubiquitous, there is “a movement to make AI modelling, deployment, and usage more environmentally sustainable”[2], so that companies and research labs can reduce their carbon footprint and resource depletion. This could ensure a greener and more responsible integration of AI technologies across society [5].

Educational opportunities

Higher education institutions can also play a critical role in recognising and addressing the environmental implications of generative AI and using it as a force for environmental good. Environmental scientists, for example, have identified the potential of these tools to accelerate progress on climate change research and action, as well as researching and developing more energy-efficient AI algorithms and hardware [6, 7].

Others have acknowledged the potential of large language models to enhance environmental literacy, not only by making it more efficient to generate informative content but also by making it easier to adapt that content for different purposes, in different languages and at different levels, from schools to policy organisations [3].

For university teachers and their students, generative AI has the potential to transform the modern curriculum and have a real-world impact. By incorporating information and discussion in modules and programmes about the ecological consequences of these technologies, along with the experience of using them in and beyond the classroom, future generations of students could be better prepared/positioned to navigate the evolving landscape of innovative technologies and environmental responsibility.

For universities and the people who work and study in them, the challenge of this moment is to work through such tensions – between the environmental impact of generative AI and the exciting educational research, teaching and learning potential of these tools.

Now that you have completed this step, you have reflected on environmental sustainability as a critical consideration in discussions around GenAI. In the next step, you will discuss how to weigh the potential benefits of AI tools against their ethical risks.


  1. van Dis EAM, Bollen J, Zuidema W, van Rooij R, Bockting CL. ChatGPT: Five Priorities for Research. Nature. 2023;614(7947):224– 226. Available at: DOI: 10.1038/d41586-023-00288-7
  2. Kumar A, Davenport T. How to make generative AI greener. Harvard Business Review; 2023 Jul 20. [cited September 12 2023].
  3. Rillig MC, Ågerstrand M, Bi M, Gould KA, Sauerland U. Risks and Benefits of Large Language Models for the Environment. Environmental Science & Technology. 2023;57(9):3464–3466. Available from: DOI: 10.1021/acs.est.3c01106
  4. Bender EM, Gebru T, McMillan-Major A, Shmitchell S. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency; FAccT 2021. Association for Computing Machinery. 2021:610– 623. Available from: DOI: 10.1145/3442188.3445922
  5. Hodgkinson IR, Jackson T. Three steps for businesses to make AI data and compute more sustainable . OECD.AI. Policy Observatory; 7 August 2023 [cited September 12 2023].
  6. Biswas SS. Potential Use of Chat GPT in Global Warming. Ann Biomed Eng 51. 2023:1126–1127. Available from: DOI:10.1007/s10439-023-03171-8
  7. Zhu JJ, Jiang J, Yang M, Ren ZJ. ChatGPT and Environmental Research. Environmental Science & Technology. 2023 Mar 21. Available from: DOI: 10.1021/acs.est.3c01818

Join the conversation

Select one of the modules you are currently studying or teaching. How might you creatively and meaningfully integrate a discussion or activity about GenAI and issues of environmental sustainability into one of your classes or assignments? Would it reflect the risks or the opportunities, or both?

© King’s College London
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