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Climate — AI Destroys the Environment
Rapidly growing AI data centers use immense amounts of electricity, exacerbating climate change
Meysenburg 24 [Nat Meysenburg, technologist at New America's Open Technology Institute, where he works on privacy, digital security, and surveillance technologies; “How Your Conversation with ChatGPT Contributes to Climate Change”; April 2, 2024; Accessed July 22, 2024; https://www.newamerica.org/the-thread/artificial-intelligence-climate-impact/]//england
Since ChatGPT was unleashed upon humanity, tech companies have raced to put artificial intelligence (AI) into their offerings, prompting Microsoft to call 2023 “the Year of AI.” Recent reports of Google’s decision to integrate the Gemini image generator on iPhones and in Google Image searches exemplifies a pervasive trend: AI is everywhere. To be sure, the ability to generate text, images, and videos with just a few prompts—as new AI models allow you to do—is a technological marvel, but beneath the simplicity of that text box lies the massive physical reality and environmental footprint of the resources required to run these AI models.
Building a model like GPT can be resource intensive, and those costs pale in comparison to the resources needed to actually run it. AI’s power consumption comes in two phases: the energy required to train the model, and the energy required to run the model.
But what is the exact energy toll of training and running AI systems? While many AI developers don’t share numbers on the carbon footprint of training models, Meta gave us a sense when it shared its open-sourced model, LLaMA. According to Meta’s numbers, training this large language model (LLM)—the same kind of AI used for things like ChatGPT—produced an estimated 539 tons of carbon dioxide (CO2). That is equivalent to the electricity used by more than 100 hundred average U.S. homes in a year. Training GPT-3, a predecessor of ChatGPT, is estimated to have used as much electricity as 177 homes.
That seems like a lot of energy for one AI model, but that only accounts for the training and not the energy it costs to run these models, especially when power demands increase as people actually use them. To better understand why AI has such a huge carbon footprint, it is helpful to think about the heavy infrastructure required for such tech to work.
“To prevent new AI models from becoming ecological catastrophes, AI experts need to be intentional about how they design them.”
Most modern AI setups involve running models on several servers living in one or more data centers. A typical server built for AI contains multiple Graphics Processing Units (GPUs)—a type of chip originally developed for video game graphics that has become a cornerstone of AI. These optimized servers use a lot of power and generate a lot of heat, and just like your laptop, servers will produce more heat when they are used more intensely.
In a data center, these power-hungry heaters are stacked in six- to eight-foot tall towers organized in rows. These massive stacks must be aggressively cooled, lest it all catch on fire. Cooling the servers requires numerous fans attached to the servers themselves, along with blasts of air conditioning (or swamp cooling) for the entire building—leaving the inside of these data centers loud and cold. Because servers are supposed to be running 24/7, data centers also tend to have large, onsite diesel generators that kick in when power from the grid goes out. Between the power used by the servers and the energy it takes to cool them down—as well as the possibility that sometimes that energy is generated directly by burning diesel—the environmental impact of a data center can be significant. According to the International Energy Agency, in 2020, data centers were responsible for nearly 1 percent of energy-related greenhouse gas emissions.
Data centres ‘straining water resources’ as AI swells.
Taha and Olufemi 23 — Abdallah Taha, science author at scidev.net, and Alfred Olufemi, covers central and northern Africa for Law.com International, writing about lawyers and legal issues on the continent, 2023 (“Data centers 'straining water resources' as AI swells,” phys.org, November 16th, Available Online at https://phys.org/news/2023-11-centers-straining-resources-ai.html, Accessed 07-22-2024)
However, “pressure on existing limited water resources [from data centres] may have significantly strained the provision of adequate clean water,” she added.
“The establishment of AI and data centres in Nigeria can facilitate technological development, foster collaboration with global tech companies, and generally improve economic growth,” said Ajijola.
“However, it can exacerbate the strain on the already limited water supplies and potentially impact the people’s access to water.”
AI’s ‘water footprint’
Conventional data centres already use large amounts of water for their cooling processes. But these may need to increase their cooling capacity to support the vast computing resources and storage requirements needed to support new AI applications.
Kaveh Madani, director of the UN University Institute for Water, Environment and Health and ex-vice-president of the UN Environment Assembly Bureau, explained: “AI requires high-performance processes and that results in more electricity and water use in the case of AI data centres when compared with conventional data centers.”
Dedicated AI data centres are springing up, mostly in the US, but increasingly also in the global South, where water resources are stretched.
Stanford University’s AI Index Report 2023 revealed that the environmental impact of training AI models is unprecedented as the adoption of AI skyrockets. The proportion of companies using AI more than doubled between 2017 and 2022, according to the report.
Madani said AI experts were becoming increasingly aware of AI’s environmental footprint.
“The public and the decision-makers must know about such footprints to proactively develop […] footprint-reduction measures,” he told SciDev.Net.
Diagram showing: AI water consumption
AI systems require a lot of data to train and operate. This data is stored in data centres, which are large facilities that house computer systems and other equipment.
The data centres are the backbone of the digital world, providing the computing power and storage space that AI systems need to function. They power our online activities, such as social media, online shopping, streaming services, online gaming and video conferencing.
Even after adopting best practices, machine learning takes up around 10 to 15 per cent of the total data centre energy at Google, according to a paper by computer scientists at the US tech company.
Water consumption at the firm’s data centres increased more than 60 per cent in the last four years, from 3,412 million gallons in 2019 to 5,565 million gallons in 2022.
Shaolei Ren, associate professor in the department of electrical and computer engineering at the University of California, Riverside, says the carbon footprint of AI models is usually documented, but “the water footprint is missing”.
“Once we have the visibility of AI’s water footprint, we can leverage the scheduling flexibility of AI to reduce it,” he told SciDev.Net.
Inside a data centre.CC0 1.0 DEED
Inside a data centre. Source: pixhere (CC0 1.0 DEED)
Why so thirsty?
Data centres use water in two ways: direct and indirect. Data centres require vast amounts of electricity for their operations and electricity generation requires significant amounts of water. In addition to this indirect impact via electricity use, data centres require considerable amounts of water for cooling.
Power plants use water to generate electricity by heating water to create steam, which then turns a turbine to generate electricity. The water used in this process is first treated to remove any contaminants that could damage the equipment.
After the water is used to create steam, it may be evaporated, discharged as effluent, or recirculated.
Data centres also use water to cool the equipment by circulating water through it, which absorbs heat. The water is then cooled and recirculated.
The amount of water used varies depending on the type of power plant and from region to region, as the water consumption depends on the weather conditions and cooling technology used.
Computer scientists interviewed by SciDev.Net say these local conditions are often not factored into decisions on where to locate data centres or what cooling systems to use.
“We need to consider the local constraints and priorities before deciding the most suitable cooling method,” Ren added.
“We need to consider not only the direct water consumption but also the indirect water consumption for electricity generation to holistically reduce the total water footprint.”
According to a recent preprint (a draft research paper that has not been peer reviewed) published by Ren and his colleagues, training GPT-3 in Microsoft’s state-of-the-art US data centres can directly consume a total of 700,000 litres of clean freshwater. They say the language prediction model needs to “drink” a 500ml bottle of water to have a simple conversation of about 20- to 50 questions and answers per user.
If this training was done in Microsoft’s Asian data centres, it would consume three times as much, according to the paper. This is because the climate in Asia is warmer and drier, which means that more water is needed to cool the data centres.
“This is extremely concerning, as freshwater scarcity has become one of the most pressing challenges shared by all of us in the wake of the rapidly growing population, depleting water resources, and aging water infrastructure,” the paper says.
Sub-Saharan Africa is one of several regions facing a water crisis, with water demand expected to almost quadruple by 2030. Yet it is becoming a hub for data centre investments.
The demand for data centres on the continent has doubled since 2016, according to the International Telecommunications Union, and it is expected to attract US$5 billion of investment by 2026, potentially putting further strain on water resources.
Lack of regulation
“This sector is not properly regulated in both the global North and global South,” said Madani from UN University Institute for Water, adding that stronger regulations, monitoring and reporting are “essential”.
“In addition, service users must become aware of the impacts of their ‘AI consumption’ on water resources,” he said.
According to the World Bank, water-related disasters account for 70 per cent of all deaths related to natural disasters.
About 2 billion people worldwide don’t have access to safe drinking water and roughly half of the world’s population is experiencing severe water scarcity for at least part of the year.
These numbers are expected to increase, exacerbated by climate change and population growth.
In developing countries, the water footprint of AI data centres is more concerning. These countries often have limited water resources and are already struggling to meet the needs of their growing populations.
The addition of a growing number of data centres to the mix could put a strain on water resources and lead to water shortages.
Mohammad Atiqul Islam, assistant professor in computer science and engineering at the University of Texas at Arlington, and one of the published preprint team, said water was rarely considered when locating data centres.
“Typically, companies care more about performance and cost; they do not take water intensity into account,” he told SciDev.Net.
“We need to ensure that the environmental costs are fairly distributed across different regions for environmentally equitable AI, which is a crucial consideration for responsible AI,” added Ren.*
Unregulated AI spreads climate misinformation and increased electricity and water usage only harms climate efforts
Seiber, Press Officer and Climate Disinformation Spokesperson at Friends of the Earth, 2024
[Erika, “Report: Artificial Intelligence A Threat to Climate Change, Energy Usage and Disinformation” Friends of the Earth, March 7, 2024, https://foe.org/news/ai-threat-report/ Accessed 7.22.24 GDI-HB]
WASHINGTON – Today, partners in the Climate Action Against Disinformation coalition released a report that maps the risks that artificial intelligence poses to the climate crisis.
Topline points:
AI systems require an enormous amount of energy and water, and consumption is expanding quickly. Estimates suggest a doubling in 5-10 years.
Generative AI has the potential to turbocharge climate disinformation, including climate change-related deepfakes, ahead of a historic election year where climate policy will be central to the debate.
The current AI policy landscape reveals a concerning lack of regulation on the federal level, with minor progress made at the state level, relying on voluntary, opaque and unenforceable pledges to pause development, or provide safety with its products.
“AI companies spread hype that they might save the planet, but currently they are doing just the opposite,” said Michael Khoo, Climate Disinformation Program Director at Friends of the Earth. “AI companies risk turbocharging climate disinformation, and their energy use is causing a dangerous increase to overall US consumption, with a corresponding increase of carbon emissions.”
“We are already seeing how generative AI is being weaponized to spin up climate disinformation or copy legitimate news sites to siphon off advertising revenue”, said Sarah Kay Wiley, Director of Policy at Check My Ads, “Adtech companies are woefully unprepared to deal with Generative AI and the opaque nature of the digital advertising industry means advertisers are not in control of where their ad dollars are going. Regulation is needed to help build transparency and accountability to ensure advertisers are able to decide whether to support AI generated content.”
“The evidence is clear: the production of AI is having a negative impact on the climate. The responsibility to address those impacts lie with the companies producing and releasing AI at a breakneck speed,” said Nicole Sugerman, Campaign Manager at Kairos Fellowship. “We must not allow another ‘move fast and break things’ era in tech; we’ve already seen how the rapid, unregulated growth of social media platforms led to previously unimaginable levels of online and offline harm and violence. We can get it right this time, with regulation of AI companies that can protect our futures and the future of the planet.”
“The climate emergency cannot be confronted while online public & political discourse is polluted by fear, hate, confusion and conspiracy,” said Oliver Hayes, Head of Policy & Campaigns at Global Action Plan. “AI is supercharging these problems, making misinformation cheaper and easier to produce and share than ever before. In a year when 2 billion people are heading to the polls, this represents an existential threat to climate action. We should stop looking at AI through the “benefit-only” analysis and recognise that, in order to secure robust democracies and equitable climate policy, we must rein in big tech and regulate AI.”
“The skyrocketing use of electricity and water, combined with its ability to rapidly spread disinformation, makes AI one of the greatest emerging climate threat-multipliers, said Charlie Cray, Senior Strategist at Greenpeace USA, “Governments and companies must stop pretending that increasing equipment efficiencies and directing AI tools towards weather disaster responses are enough to mitigate AI’s contribution to the climate emergency.”
Previously, the coalition submitted letters to President Biden and Senator Chuck Schumer that call on them to implement climate concerns into proposed AI legislation. The letters echo recommendations made in the report, including:
Transparency: Companies must publicly report on energy usage and emissions produced, assess any environmental justice concerns related to developing AI technology and disclose how their AI models produce information in a way that prioritizes climate science.
Safety: Companies must be able to publicly demonstrate their products are safe for users and the environment. In addition, governments should develop standards on AI safety reporting and invest in research that maps the risks AI poses to the spread of climate disinformation.
Accountability: Governments should enforce rules on investigating and mitigating the climate impacts of AI with clear, strong penalties for noncompliance. Companies and their executives must be held accountable for any harms that occur as a result of their products.
AI increases carbon emissions heavily.
Keller, Donoghoe, and M. Perry 24 [Joseph B. Keller is a visiting fellow in Foreign Policy, affiliated with the Strobe Talbott Center for Security, Strategy, and Technology and Artificial Intelligence and Emerging Technology Initiative. His research interests include global governance strategies for machine learning and artificial intelligence, policy implications for emerging technologies in the global south, and the geopolitical consequences of climate change. Manann Donoghoe is a Senior Research Associate in Brookings Metro, conducting research for the Valuing Black Assets Initiative. His work focuses on majority-Black neighborhoods and cities, identifying the possibilities and constraints for supporting secure, safe, and opportunity-rich communities. Manann’s expertise is in environmental and social justice, previously working to develop international social protection responses to the climate crisis, including with the World Bank, the Red Cross Red Crescent Climate Centre, and the Australian Department of Foreign Affairs and Trade. Manann has also worked as an environmental economist for the Australian Department of Agriculture and has published on issues related to environmental racism, COVID-19 economic and housing policies, food security, sustainability, and water policy. He holds a master’s degree in human geography and environmental governance from the University of Oxford and a bachelor’s degree in resource economics from the University of Sydney. Andre M. Perry is a Senior Fellow at Brookings Metro, a scholar-in-residence at American University, and a professor of practice of economics at Washington University. A nationally known and respected commentator on race, structural inequality, and education, Perry is the author of the book “Know Your Price: Valuing Black Lives and Property in America’s Black Cities,” which is currently available wherever books are sold. Perry is a regular contributor to MSNBC and has been published by numerous national media outlets, including The New York Times, The Nation, The Washington Post, TheRoot.com and CNN.com. Perry has also made appearances on HBO, CNN, PBS, National Public Radio, NBC, and ABC. Perry’s research focuses on race and structural inequality, education, and economic inclusion. Perry’s recent scholarship at Brookings has analyzed Black-majority cities and institutions in America, focusing on valuable assets worthy of increased investment. “The US must balance climate justice challenges in the era of artificial intelligence.” (January, 29, 2024) https://www.brookings.edu/articles/the-us-must-balance-climate-justice-challenges-in-the-era-of-artificial-intelligence/#:~:text=Despite%20their%20operation%20in%20a,form%2C%20through%20rising%20energy%20demands. //GDI KR//]
Meanwhile, the extraordinary progress of AI carries a growing, but often hidden, environmental footprint. The proliferation of tech applications such as generative AI and the expansion of ubiquitous cloud services remain tethered to digital infrastructure and supply chains. This elevates the challenge of stimulating economic growth without comparable increases in emissions—a task that University of Florence Assistant Professor Roberto Verdecchia has said is “physically impossible.”

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