Data centers can harm the environment not only by what they consume, but also by what they produce. E-waste, or electronic waste, refers to discarded servers, network equipment, and other hardware that are no longer functional. Some of this waste contains hazardous materials like lead that can pollute the environment and cause harm to plants, wildlife, and humans, especially those in underdeveloped nations, where a lot of the waste tends to land.
Generative AI has the potential to increase e-waste in data centers. During training, AI requires a lot of GPUs and GPU-enabled servers that often becomes obsolete afterward. There aren’t many use-cases for it unless the company plans to retrain the AI. Essentially, there will be more GPU-based hardware up for disposal, and some of it will inevitably be discarded in irresponsible or inefficient ways. By 2030,
As artificial intelligence technology is deployed in an increasingly wide range of use cases, more people in the industry are beginning to investigate possible applications of AI for climate change. At the same time, more attention is being paid to finding ways to mitigate the environmental impact of AI and the data centers that power it. Due to its unusually high energy consumption for training and operation, AI also acts as a contributor to greenhouse gas emissions, waste, water shortages, and other environmental issues. Understanding the complicated relationship between AI technology and climate change can help you better understand how companies and governments might turn it into a boon for the environment.
KEY TAKEAWAYS
•AI systems require data centers to operate, and these huge facilities devour electricity and water while also generating e-waste—all of which negatively impacts the environment. (Jump to Section)
•More sustainable AI infrastructure is essential if we’re to reduce the carbon emissions and environmental stressors produced by artificial intelligence technologies. (Jump to Section)
•
Despite AI’s high energy consumption, the technology is also a powerful ally in the fight against climate change and its negative effects, from helping buildings run more efficiently to predicting dangerous weather events. (Jump to Section)
Google no longer claims to be carbon neutral, thanks to the company’s generative AI systems—which require massive amounts of energy to train, deploy, and operate, leading to a rise in carbon emissions. And the problem is only getting worse. According to Intelligent Computing, the computing power required for AI is doubling every 100 days. AI systems consume energy in a variety of ways—the following are two of the most common:
Training: Teaching AI requires using massive computational power, sometimes thousands of GPUs simultaneously over millions of hours. Systems like GPT-3 also have to go through multiple rounds of training.
Deployment and Operation: Once a tool like Chat-GPT is deployed, it’s always on, providing instant service to users worldwide. This service requires predictive technology, which requires a lot of energy to run, and the data centers supporting the AI operations are huge energy users.
Overall, the high energy consumption of AI technology poses the risk of overshadowing any benefits these advanced tools provide in the fight against climate change. To keep that from happening, companies have to adopt renewable energy sources and find innovative ways to reduce the carbon footprint of these tools.
Data Centers and Their Environmental Footprints
AI systems use data center infrastructure to store and process vast amounts of data. These data centers need electricity for everything from facility lighting to liquid cooling to computing power, an energy requirement that is extremely high for ongoing, complex AI computations. In addition to electricity, data centers also use large quantities of water to keep machines from overheating, having a large impact on our water supplies.
Data Center Energy Consumption
The Office of Energy Efficiency and Renewable Energy calls data centers “one of the most energy-intensive building types, consuming 10 to 50 times the energy per floor space of a typical commercial office building.” Currently, data centers use 1 to 2 percent of overall power worldwide. Goldman Sachs predicts that number will double, rising to 3 to 4 percent by the end of the next decade, mostly due to the large demands of the AI systems they support. AI contributes to increased energy consumption in data centers in the following ways:
Advanced Hardware: High-performance GPUs and specialized AI chips, which consume more power, are required to process AI workloads efficiently.
Predictive Functionality: Running AI models for real-time predictions or recommendations demands constant computational resources, raising power usage.
Data Storage: AI systems require massive data storage, increasing the energy demand for maintaining and cooling storage servers.
As generative AI tools become increasingly entrenched in our technology, data center energy consumption will only continue to rise unless there are large improvements in energy efficiency. Examples include AI algorithms that require less computing power, along with the use of solar panels and other renewable resources to power these data centers.
Data Center Water Usage
The data centers that support AI technologies use water to support various industrial-scale cooling systems that keep equipment and servers at an ideal temperature and prevent overheating and system failure. Water usage at data centers has been rising alongside the increase in AI system training and deployment.
One of the most water-intensive cooling methods at these centers is evaporative-based cooling, where water is evaporated to cool the air surrounding the equipment. It’s not uncommon for a cooling tower to use millions of gallons of water per year to cool a single data center. Such cooling approaches have significant costs to the water supply, which is already stressed. According to the World Wildlife Organization, 2.7 billion people experience water scarcity at least once per month every year. As climate change continues to heat the planet and cause desertification, this number could rise dramatically.
Fortunately, there are more sustainable cooling practices being used, which recycle water and as a result waste far less of it. For example, liquid immersion cooling submerges servers into a non-conductive liquid to cool them down.
Microsoft has seen its own impact on water supplies grow dramatically in the past few years. While it’s a key player in AI, it’s just one of hundreds of companies having such an effect.
Data Center E-Waste Generation
Data centers can harm the environment not only by what they consume, but also by what they produce. E-waste, or electronic waste, refers to discarded servers, network equipment, and other hardware that are no longer functional. Some of this waste contains hazardous materials like lead that can pollute the environment and cause harm to plants, wildlife, and humans, especially those in underdeveloped nations, where a lot of the waste tends to land.
Generative AI has the potential to increase e-waste in data centers. During training, AI requires a lot of GPUs and GPU-enabled servers that often becomes obsolete afterward. There aren’t many use-cases for it unless the company plans to retrain the AI. Essentially, there will be more GPU-based hardware up for disposal, and some of it will inevitably be discarded in irresponsible or inefficient ways. By 2030, e-waste generation will reach 2 million metric tons globally, according to Statista—about a 33 percent jump from 2022. To combat this problem, data centers will have to implement e-waste management best practices such as proper recycling.
The Need for Sustainable AI Infrastructure: Best Practices
Considering the amount of water, energy, and electricity consumed by artificial intelligence, there’s an urgent need for more sustainable AI infrastructure if the technology is going to provide a net-positive effect on climate change in the coming decades. The following are some of the most common sustainability best practices for AI infrastructure:
Renewable Energy for AI Infrastructure: By powering data centers with renewable energy sources like solar, wind, and hydroelectric power, the carbon footprint of AI operations can be significantly reduced. This shift away from fossil fuels helps to minimize greenhouse gas emissions and supports a transition to a more sustainable energy grid.
Energy-Efficient AI Hardware: Designing AI hardware with lower power consumption, such as specialized chips, can reduce the energy required for complex computations. This makes AI systems more sustainable and limits data center emissions and energy usage. It’s also important to use existing models if possible, rather than training a new one, a resource-intensive process.
Sustainable AI System Design: Implementing algorithms and software that require less computational power—such as smaller or simpler models—helps to optimize energy use during AI model training and deployment. As a result, these designs make AI technologies more environmentally friendly.
While AI companies, government agencies, and data centers will have the greatest control over making AI sustainable, end-users have some power as well. We’ll need to treat AI like any other tool that requires energy and produces waste, using it only when necessary. As AI gains adoption, this issue of usage will move to the forefront.
5 Major Ways AI Can Help Address Climate Change
Despite contributing to greenhouse gas emissions through energy consumption, AI can also help us prevent climate change and deal with its consequences through climate predictions, various forms of environmental monitoring, and new technology that allows facilities, farms, and businesses to operate in a more environmentally-friendly manner.
Enhanced Weather and Climate Forecasting
As climate change warms up the atmosphere, dangerous storms, changes in sea level, and catastrophic weather events become increasingly common. Artificial intelligence can be used to forecast these events by analyzing vast amounts of meteorological data such as historical weather data, satellite imagery, ocean temperatures, and real-time sensor data. This crystal ball into the future enables communities and facilities to prepare early for potentially destructive events and protect infrastructure and lives. That could be evacuating an area before a flood or enacting precautionary measures at a power plant prior to a category three hurricane. Meanwhile, accurate climate modeling will empower policymakers to make smarter plans for mitigating the risks of climate change.
AI also assists with real-time weather monitoring by analyzing continuous streams of data from sources like satellites, weather stations, and sensors. Processing this data rapidly, it can spot patterns and anomalies that signal changing weather conditions, such as storms, temperature shifts, or wind changes. This is vital for industries like agriculture, aviation, and emergency services that need immediate information about the weather.
Tracking and Addressing Deforestation
AI-powered satellite image analysis is being used to track deforestation in a near real-time manner. This intel helps conservation and wildlife organizations spot areas of deforestation and even find illegal logging practices. Predictive deforestation modeling can actually help organizations identify areas ripe for deforestation. These algorithms crunch data such as land use patterns and population to identify at-risk locations.
When combined, this real-time and predictive intelligence helps organizations create more data-driven deforestation prevention and remediation strategies while focusing their efforts on the most vulnerable locations. AI can also aid in reforestation, analyzing land use and terrain to find optimal areas for reforestation projects. Acting as an analyst, it can even suggest the best types of foliage for these projects.
Pollution Monitoring and Waste Management
AI can enhance pollution monitoring and waste management by analyzing vast data from sensors, satellites, and IoT devices to identify pollution sources and trends in real time. Additionally, it can predict pollution levels and assess air and water quality, helping authorities respond quickly to hazardous situations.
In waste management, AI-driven robots and computer vision can sort recyclable materials (including e-waste) more efficiently, reducing contamination and landfill usage. By identifying patterns in waste generation, AI can even help businesses and urban planners design better recycling strategies and improve sustainability efforts, ultimately reducing environmental impact.
Ocean Cleanup and Marine Conservation
AI offers huge potential for marine conservation due to its ability to identify plastics and other forms of pollution—particularly hard-to-detect microplastics—and remove them from the ocean. Recently, two graduate students in Hong Kong created an autonomous boat, Clearboat Neo, that uses AI imaging to separate debris from marine life, which it then scoops up onto a conveyor belt. A second camera then evaluates the debris and logs its type.
While cleaning rivers, they also get data about the pollution such as its source, whether it’s recyclable, and how it got there. Perhaps more important than the trash they remove, this information can help organizations and governments prevent water pollution going forward.
Renewable Energy Optimization
Artificial intelligence can optimize renewable energy by improving its production, storage, and distribution. Through predictive analytics, AI forecasts weather conditions, enabling wind and solar farms to adjust operations for maximum energy capture. It can also optimize grid integration, balancing energy supply and demand in real time, reducing reliance on fossil fuels during peak usage.
In energy storage, AI can intelligently manage battery charging and discharging, ensuring energy availability even when renewable sources are intermittent. Additionally, AI-powered asset monitoring systems can be used to track and identify maintenance needs for wind turbines and solar panels, reducing downtime and extending their lifespan, thus improving the efficiency and reliability of renewable energy systems.
3 Courses To Learn More About AI And Climate Change
To learn more about the relationship between AI and climate change, the following online Oxford University, Coursera, and Stanmore UK courses can boost your knowledge and expertise.
Oxford University’s AI for Agriculture Technology and Climate Change
This online course is designed to teach you how to apply AI techniques and technologies to issues in agriculture and climate change. You’ll learn about crop analysis, AI for Good, yield forecasting, agtech applications, and more. It’s best suited for professionals working in agtech, agriculture, sustainability, and the food value chain who want to introduce AI into their workflow. The course cost is £875 (about $1150 US).
AI and Climate Change is a beginner-level online course that teaches you about how AI is being used to mitigate and adapt to climate change. Highlights include case studies about wind power forecasting and computer vision for biodiversity monitoring. Overall, it takes about 14 hours to complete and should give you a solid foundation in this topic. The course is free of charge.
Stanmore UK’s Postgraduate Certificate in AI for Sustainable Development
The Postgraduate Certificate in AI for Climate Change Solutions will give you the foundational skills necessary for tackling the challenges of climate change with artificial intelligence. Highly comprehensive, the course covers climate modeling, environmental monitoring, sustainable solutions, and policy implications. Professionally-focused, this is a great course if you’re looking to start a new career in the field of AI climate change. It costs about $183 for the one-month fastrack version, or $117 for the two-month version.
AI technology can be used to address climate change challenges in myriad ways, from predicting climate patterns and weather events to identifying areas at risk for deforestation. By analyzing vast amounts of climate data, AI can also provide organizations with insights that help them protect and improve the natural environment.
How is AI Used in Weather and Climate?
Artificial intelligence is used in weather and climate to assist in weather forecasting and climate modeling. For example, by analyzing atmospheric data, pressure sensors, and other relevant information, AI could warn communities of a possible hurricane, enabling them to prepare ahead of time.
Is AI Really About to Devour all our Energy?
Although AI systems require a lot of energy to run, they are not going to devour all our energy any time soon. Data centers, which support AI technology, still only consume 1-2% of all energy worldwide. To keep that number from rising, however, AI companies must embrace more sustainable AI infrastructure.
Does AI Increase Carbon Footprint?
Artificial intelligence can increase the carbon footprint due to the vast amounts of energy needed to train and operate complex AI models. Notably, the data centers that support generative AI regularly rely on fossil fuels to function properly, which can contribute to greenhouse gas emissions.
Can AI Solve Environmental Problems?
Artificial intelligence can help solve environmental problems by providing conservationists, wildlife specialists, nonprofits, and governments with new technologies like deforestation monitoring solutions, ocean pollution cleanup robots, and climate modeling tools.
Bottom Line: The Double-Edged Sword of AI for Climate Change
Artificial intelligence has the power to both harm and help our environment. It can contribute to greenhouse gas emissions and the carbon footprint with its high energy usage and e-waste, and help us mitigate and manage climate change through the various technologies it makes possible, from pollution monitoring to highly accurate weather forecasting. Ultimately, it’s up to us to hold policymakers and firms accountable for their actions, and to promote more sustainable AI infrastructure so the costs to the environment don’t outweigh the benefits.
