Environmental Impact of AI Websites in Germany

Key Takeaways

• ChatGPT generates the most CO₂ (69.9k kg) not because it is the highest per visit, but because it has the most users (194.3M visits). This shows that AI’s environmental impact is not only a carbon emission issue, but also a scale issue.
• Some platforms are becoming more efficient, like Gemini and DeepSeek. Optimized hardware, smaller models, and cleaner energy sources are reducing emissions per query.
• 100 kg of CO₂ is already equal to the amount of carbon that 5 mature German trees can absorb in a year. It is also roughly equivalent to 25 years of smartphone use and a short-haul flight within Germany per person.

Germany AI Websites’ Environmental Impact

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Artificial intelligence is transforming how we work and use digital tools, but it also comes with a growing environmental footprint. Behind each interaction are data centres that consume large amounts of energy and resources.

The environmental impact of AI websites in Germany is not just about how efficient they are, but also how often they are used.

chatgpt.com stands out immediately. With 194.3 million visits, it generates around 69.9 thousand kg of CO₂.

To help you imagine, 100 kg of CO₂ is roughly equivalent to:

• 25 years of smartphone use
• 1.6 years of video streaming
• 1 short‑haul flight within Germany (400–800 km, per person)
• 400-500 km of driving a combustion-engine car
• CO₂ absorbed by 5 mature German trees in one year

At this scale, ChatGPT’s total emissions are comparable to travelling from Berlin to Munich a thousand times. 

This level of CO₂ consumption is evident across AI websites.

• Gemini (27.4M visits) → 11.5k kg CO₂
• DeepL (23.6M visits) → 10.9k kg CO₂
• Claude (11.9M visits) → 6.3k kg CO₂

Some platforms have higher emissions per visit, like Claude (0.53g), but lower total impact because they are used less.

Others, like Perplexity (0.14g) or DeepSeek (0.04g), show lower emissions per visit, but still contribute depending on how often they are used.

Most of the environmental impact does not come from loading the website.

It comes from what happens behind it.

AI tools rely on data centres to process each request. These already consume around 4% of Germany’s gross power consumption in 2024, and AI workloads are a growing share.

Each interaction triggers compute-intensive processing, which uses more energy than a normal search. A single query can use around 0.3 to 1 watt-hour of electricity, depending on the system.

Some AI platforms appear “greener”

Some platforms show lower emissions per visit because they are more efficient by design.

Gemini (Google) is one example. A typical query uses about 0.24 Wh, thanks to:

• specialized hardware (TPUs)
• optimized model design
• access to lower-carbon electricity

DeepSeek reduces energy use differently.

It has an efficient model design designed to use less computing to reduce energy use. But actual emissions still depend on where the servers are located. If powered by a carbon-intensive grid, emissions can still be higher.

There are also simpler reasons.

Some platforms have lighter websites, which reduces front-end emissions. Others are used for short, simple prompts, while more complex tasks (like coding or long responses) use more energy.

The “CO₂ per visit” values in the table mainly reflect website loading (front-end activity). They capture only a part of the AI processing behind each query, which is the largest source of emissions.

Even so, the pattern is clear.

ChatGPT is not the most carbon-intensive per visit, but its scale of use drives the largest total impact.

At the same time, some platforms are becoming more efficient. Tools like Gemini and newer AI models show that lower energy use per query is possible, especially with optimized hardware and cleaner energy sources.

But efficiency alone is not enough.

As AI becomes part of everyday work and digital activity, total emissions continue to rise. The environmental impact is still concentrated in the data centres and systems that power these tools.

This means the future impact of AI depends on two things: how efficiently it runs, and how widely it is used.

Greener infrastructure, better model design, and cleaner electricity can reduce emissions per interaction.

In the end, one of the most important questions is how AI’s growing use can be managed in a way that keeps its environmental cost under control.