The growth of AI has significantly increased the demand for data processing capabilities, leading to the expansion of data centres globally.

As these data centres consume vast amounts of energy and water—and heavily contribute to CO₂ emissions—they pose a significant environmental challenge.

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They are distributed globally, with approximately 33% located in the United States. Major data centre hubs in the US include California, Texas, and Virginia. China hosts approximately 10% of the world’s data centres. About 16% of all data centres are located in Europe, with Ireland seeing significant growth, which has raised concerns about grid capacity. Key data centre markets in Europe include Germany, France, the Netherlands, and Ireland.

Data centre overview

Data centres are facilities used to house computer systems and associated networking equipment to capture, store, analyse, and re-transmit data.

In recent years, there has been a growing trend to strategically locate data centres based on cost and energy considerations. For instance, companies are increasingly building servers in regions with low electricity costs and high renewable energy sources. Another approach is to put data centres in locations where minimal energy is needed for cooling, such as Arctic regions or underwater like in Microsoft‘s 2015 project, Project Natick.

However, these innovative placements often present challenges related to latency. When the data centre is far removed from the end user, the transfer of data takes longer. Lowering latency is key for tasks that require real-time communication resulting in geographic diversification of the data centre market.

AI resource demand

Data centres are critical infrastructures in today’s digital world. However, they have a big environmental impact, which was exacerbated by the advent of generative AI. Development and training of large language models (LLMs) are being done in data centres. The power and complexity of these AI models are reflected in the greater training time and energy required.

Firstly, the negative environmental impact is due to the exponential growth of data centres. As they increase in size and complexity, more resources are dedicated to their construction and deployment.

The energy usage of power data centres must also be considered. The IEA reported that, between 2015 and 2022, their energy use grew by 20-70%. In addition to this, data centres are significant drivers of electricity demand. Their global consumption is set to reach 1000 Terra Watt hours (TWh) by 2026, up from 460 TWh in 2022 (IEA).

Data centres also require significant water usage for cooling, as they have power-intensive IT equipment continuously running. If equipment overheats, malfunctions and breaks down may occur. Training LLMs is water-intensive, as water is a common cooling method.

With the AI boom, this usage is becoming more prominent. From an ESG viewpoint, water cooling has major impacts across all scopes. This includes water used in cooling towers, for electricity generation, and its usage throughout the supply chain.

Finally, we also need to consider the electronic and chemical waste from data centres. GlobalData’s Deep Dive into the Semis-AI-ESG Trinity expects that investments in data centres optimised for AI workloads are expected to increase rapidly. As the carbon footprint of data centres grows, it will become a significant issue for AI adoption, especially concerning ESG targets.

Growing concerns

The AI Index Report 2024 highlights growing concerns about the environmental impact of AI training and use.

For example, Meta’s Llama 2 70B model released about 291.2 tons of carbon during training. This is nearly 291 times the emissions from one round-trip flight from New York to San Francisco and about 16 times the annual carbon emissions of an average American. Additionally, the emissions from using LLMs can surpass those from training when they are queried millions of times daily.