How can you reduce the environmental impact of using AI?
10 actions ranked by effectiveness

Not all actions to reduce the environmental impact of using artificial intelligence (AI) are equally effective. For someone who generates 1 video per week, generating 2 times fewer avoids about 1 kgCO2e per year (1 kg of CO2 equivalent) with our reference generator, equivalent to 7 km by car, or 290 g with a median generator. No longer thanking your assistant (1 thank-you per day) avoids 39 g, which is 26 times less.

We quantified 10 actions for a usage profile that we chose: 10 questions per day, 5 images and 1 video of 5 seconds per week. Of these, 3 actions matter, 5 help and 2 change almost nothing. By our calculation, this profile emits about 2.8 kgCO2e per year from server electricity alone, equivalent to 20 km by car.

1The 10 actions to reduce the environmental impact of AI, from most to least effective

How the actions are grouped

An action "matters" if it can avoid at least 1 kgCO2e per year and "helps" if it can avoid at least 50 g. Running a model (the software that produces the answers) on your own computer is among the actions that help, yet it can also increase emissions.

How it is calculated

For each action we assume a behaviour of our own choosing, which serves as its reference value: generating 2 times fewer videos, shortening 1 answer in 2, dropping 1 thank-you per day. 7 actions are based on a published measurement; for the other 3 (whether AI is needed, follow-ups and stored conversations) we found none. Every figure in grams or kilograms in this article comes from our own calculation, using global average electricity.

This profile is more active than average: in July 2025, the average weekly active ChatGPT user sent 3.6 messages per day, according to our calculation from the figures in a study co-authored by OpenAI. We found no published figure for the number of videos generated per user.

The ranking therefore depends on the profile. For an intensive user, the total rises to 14 kgCO2e per year, of which 10 kg comes from video, which stays at the top; 8 actions out of 10 can then avoid more than 1 kg. For an occasional user (2 questions per day, 1 image per week, no video), the total falls to 160 gCO2e per year, no reference value exceeds 35 g and reasoning mode moves to the top. Within a group, the order carries no meaning where the ranges overlap, and the savings cannot be added together.

2The actions that matter most: video, reasoning mode and whether AI is needed

Generate fewer videos, and shorter ones: about 1 kgCO2e per year

In our profile, the 52 videos of the year emit 2 kgCO2e out of 2.8, more than 70% of the total. This figure rests on a single generator: the most downloaded one on Hugging Face (a platform for open models, meaning models that can be downloaded freely), and the second most energy-hungry of the 7 measured in the study it comes from. With the median generator, the one in the middle of the ranking (it produces lower-definition videos), generating 2 times fewer would avoid only about 290 g.

Duration matters even more than the number of videos: according to the same study, a video 2 times shorter needs about 4 times less computation, on the generator tested. ADEME, the French Agency for Ecological Transition, lists among its main principles of use for users that video generation must be limited and meet a specific need. Our article on the energy used by an AI-generated video goes into detail.

Do not leave reasoning mode on by default: from 0.4 to 14 kgCO2e per year

Reasoning mode makes the model work through intermediate steps before answering: it writes much more text, and therefore uses more energy. PEReN, the French state's digital regulation expertise centre, measured open models for Arcep, the French electronic communications regulator: with reasoning mode on, they use up to 92% more energy on average, and up to 849% more on a coding task.

The Hugging Face AI Energy Score ranking compares 2 groups of models: those that reason consume 30 times more on average than the others. For 3 models tested with and then without reasoning, it even measures 150 to 700 times more.

This action is a special case: our profile includes no question in reasoning mode, so the action is an avoided addition. Switched on for the 3,650 questions of the year, this mode would add between 440 gCO2e (with the PEReN figure) and 14 kgCO2e (if we apply the factor of 30, although that compares 2 different groups of models). This is the most uncertain item in the ranking. The action itself is to reserve this mode for the questions that call for it.

Ask yourself whether AI is necessary: from 0 to 2.8 kgCO2e per year

This is the first condition of frugal AI in the framework published by AFNOR, France's national standards body, as summarised by Ecolab, a French state service: having demonstrated that AI is needed rather than a solution that consumes less. The gain is that of the use avoided, to which we give no reference value, only a range from nothing to the whole profile. Our comparison of ChatGPT and a Google search quantifies the difference that a conventional search makes; with the reference values of this series, giving up a video avoids as much as 300 questions would emit.

3The actions that help: images, model size, short answers, complete requests and local models

Generate fewer images, and smaller ones: about 160 gCO2e per year

The 260 images of the year emit 330 gCO2e if we take the average of the generators measured. Generating 2 times fewer avoids 160 g, and 2 times less again with a median generator. Size matters too: according to a study from the University of Florence, an image 2 times smaller on each side needs 1.3 to 4.7 times less energy, depending on the generator. See our article on the footprint of a generated image.

Choose the smallest model that does the job: about 130 gCO2e per year

According to a study from Université Côte d'Azur, choosing the smallest sufficient model everywhere would reduce the energy consumed by AI in the use phase by 27.8% overall. We apply this global average to our questions, for want of a better one. The gap between 2 models can be much larger: small models specialised in one task (such as summarising, translating or answering questions) consume 15 to 50 times less than a general-purpose model according to UNESCO, although they do not suit every use. Our ranking of ChatGPT, Claude and Gemini compares the models of the main providers.

Ask for short answers: about 130 gCO2e per year

UNESCO and University College London measured the effect of length on an open model: an answer 2 times shorter uses 54% less energy, whereas a question 2 times shorter saves only 5%.

On the same model run on another machine, writing a token (a word or part of a word) takes about 35 times more energy than reading one, according to our calculation from the measurements of Hugging Face researchers. We assume that 1 answer in 2 is shortened by half, which avoids 130 gCO2e per year. ADEME also considers it beneficial to specify the length or quality of the content you want.

Write one complete request rather than follow-ups: about 95 gCO2e per year

Each follow-up makes the model write a new answer. According to ADEME, precise queries will require fewer repetitions and new queries. We count 1 follow-up avoided for every 5 questions, or 95 gCO2e per year. This is an assumption: we found no published measurement.

Attaching a long document costs more: according to an estimate by the research institute Epoch AI, having the model read 100,000 tokens, or about 80,000 words, uses about 40 Wh (watt-hours), as much as 130 questions by our calculation. According to the same source, this cost is paid only once per conversation, so attach only the pages you need.

Run a small model on your own computer: a loss or a gain

With the same model, running it locally comes out worse: according to a study carried out on a high-end desktop computer, its chip needs 1.6 to 2.3 times more energy per request than a chip in a data centre, the building where the servers run. The gain comes from a smaller model: according to our calculation based on the measurements in another study, a lightweight model on a laptop consumes about 4 times less than a ChatGPT question, our reference, for an answer of equal length.

This gives a range from 620 gCO2e more to 360 g less per year, for the same electricity, according to our calculation. In France, electricity emits at least 8 times less than the world average, according to the emission factors of ADEME and the International Energy Agency (IEA): the balance becomes favourable if the online service runs on electricity close to that average. Our article on AI presented as eco-friendly examines this case further.

4The actions that change almost nothing: thank-yous and deleted conversations

Stop saying thank you: 39 gCO2e per year

In April 2025, a user wondered how much electricity people's thank-yous cost OpenAI. Its chief executive, Sam Altman, replied, as the press reported and without any calculation to back it up: "tens of millions of dollars well spent - you never know".

In January 2026, researchers at Hugging Face published a measurement on an open model: a thank-you consumes 0.245 Wh, including the model's answer. 1 thank-you per day adds up to 39 gCO2e per year, or 270 metres by car. A year of daily thank-yous uses about as much energy as a single video of 5 seconds, which equals about 370 thank-yous.

Delete your conversations: less than 10 gCO2e per year

A year of questions and answers takes up around ten megabytes of text, and the generated images and videos a few hundred. Based on the consumption of a data centre disk and 3 copies of each file, keeping this data for a year takes about 17 Wh for the disk alone, which is less than 10 gCO2e according to our calculation and our assumptions. There can be other reasons to delete your conversations, such as protecting your privacy.

5In a company: choosing tools and setting rules of use

For an organisation, the AFNOR framework on frugal AI, published in June 2024, lists 31 best practices, grouped into 7 themes, which the Ecolab summary ranks by ratio of gain to effort, from best to weakest. One theme concerns the need: checking that AI is a relevant way to meet it.

Our guide explains how to count AI in your company's Bilan Carbone® (the French carbon accounting method), another quantifies AI's contribution to an SME's footprint, and our calculator gives a first order of magnitude. AI comes on top of computers, screens and phones, which our guide on a company's digital carbon footprint covers.

6What individual actions do not fix: missing provider data, default AI and data centre growth

What we did not find: measurements published by the providers

This ranking combines an estimate (the energy of a question), researchers' measurements made on open models, and our assumptions. For the most widely used assistants, we found no measurement published by their providers of the energy used by an image or a video, and PEReN did not test the reasoning mode of closed models such as ChatGPT's. Users therefore cannot check the effect of their own actions. The calculation also leaves out the stages where AI has an environmental impact before the answer: chip manufacturing and model training.

AI switched on by default

Some AI is not something the user asked for: answers generated at the top of search engine results, and assistants built into software and phones. In the press release for its July 2026 opinion, ADEME asks that, in traditional services, users be able to switch off these features if they wish. It also considers training and awareness crucial to using AI thoughtfully.

The growth of data centres

One person's actions amount to a few kgCO2e per year. Data centres' electricity consumption rose by 17% in 2025, an increase of about 70 TWh (terawatt-hours), according to the IEA: our article on data centre electricity consumption gives the orders of magnitude, and the one on the 6 environmental impacts of AI looks beyond carbon alone.

7Key takeaways

• 3 actions matter: generating fewer videos, and shorter ones; not leaving reasoning mode on by default; asking whether AI is necessary.
• 5 actions help: fewer images, a smaller model, short answers and complete requests avoid from 95 to 160 gCO2e per year, while a local model can either help or make things worse.
• 2 actions change almost nothing: stopping the thank-yous (39 gCO2e per year) and deleting your conversations (less than 10 g).
• This ranking applies to people who generate videos: the profile emits about 2.8 kgCO2e per year from server electricity alone, more than 70% of it from video.

This ranking does not say whether you should use AI: our article on the arguments for and against boycotting AI addresses that question, and the one on digital pollution beyond AI places AI among other uses.

Further resources

Frequently asked questions

1Does saying thank you to ChatGPT waste energy?

Very little. According to a measurement by Hugging Face researchers published in January 2026, a thank-you consumes 0.245 Wh on an open model, including the model's answer; we found no measurement for ChatGPT. 1 thank-you per day for a year adds up to 39 gCO2e with global average electricity, according to our calculation, or 270 metres by car. About 370 thank-yous use as much energy as a single generated video of 5 seconds.

2How can I use ChatGPT in a more eco-friendly way?

As with any assistant, 3 actions matter more than the others: if you generate videos, generate fewer and shorter ones; reserve reasoning mode for hard questions; and ask yourself whether AI is necessary. Next come fewer images, a smaller model and short answers. For our regular-use profile, the total is about 2.8 kgCO2e per year for server electricity alone, according to our calculation.

3What is frugal AI?

According to the framework published by AFNOR (France's national standards body) in June 2024, as summarised by Ecolab, an AI service is frugal if it meets 3 conditions: it has been demonstrated that AI is needed rather than a solution that consumes less; best practices are adopted by the producer, the supplier and the customer of the service; and the uses aim to stay within planetary boundaries. The framework lists 31 best practices.

4Does deleting your ChatGPT conversations save energy?

Not for the climate. Keeping a year of conversations, including generated images and videos, uses about 17 Wh per year for the disk alone, according to our calculation, which is less than 10 gCO2e. Deleting them can be justified to protect your privacy.

5What is the most effective way to reduce AI's environmental impact?

For people who generate videos, it is generating fewer videos, and shorter ones. With the generator used as the reference for this series, a video of 5 seconds emits about 39 gCO2e, as much as 300 written questions, according to our calculation. In our profile, generating 2 times fewer avoids about 1 kgCO2e per year. Leaving reasoning mode on for all questions can have a larger effect: it uses from 1.9 times the energy of a simple question (PEReN measurement, an "up to" figure, on average) to 30 times (the average gap between models that reason and the others).

6Is it worth giving up AI to reduce your footprint?

On climate grounds alone, giving up all the usage in our profile avoids about 2.8 kgCO2e per year for server electricity, at the global average: the equivalent of 20 km by car. The question goes beyond climate: our article on boycotting AI reviews the other reasons put forward.

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