UsesLevel 4 of 6 occupation or activityOccupation or activity, level 4 of 6Used to calculate 11 entries, levels 5 and 6Calculated with 12 entries, levels 1 and 3Database structure 
6Territories
5Organisations
3Uses
2Machine operations
1Calculation factorsOffice worker, 1 year
One working year of an office employee or manager who uses AI: about 220 web searches with AI, 530 quick questions, 170 pages corrected or reworded, and 8 other tasks. Projet Celsius estimate.
- Text and conversation account for 53% of emissions.
- About 1,400 requests to AI over the year, or 6.4 per working day.
- An estimate: the frequencies and the share done with AI are our assumptions.
Sentence to citeOffice employee, 1 year, Projet Celsius estimate: 590 gCO2e per role per year, for 1.3 kWh of electricity, servers worldwide (458 gCO2e/kWh).
Breakdown of emissions by item
590 gCO2e in total: 1.3 kWh of electricity × 458 gCO2e/kWh (servers worldwide).
Inputs of the calculation
PUE of the data centre, Read 1,000 tokens, Write, with reasoning, Write 1,000 tokens, Quick question, Proofread a page, Web search with an AI, Web search (benchmark)
How the calculation is built
One working year of an office employee or manager who uses AI, task by task: emails, questions, texts reworked, meeting minutes, spreadsheets, presentations. The frequencies come from the content of the occupation, the use of AI from usage surveys (OpenAI, Microsoft, Crédoc); these are assumptions, and can be adjusted.
Comparison with other occupations and activities
Characteristics
| Unit | 1 person, 1 year : 220 days |
|---|---|
| Default location | world, 458 gCO2e/kWh (Ember 2025, life cycle); 11 other locations to choose from |
| Counted | the tasks done with AI over a working year, including data centre cooling |
| Not counted | server manufacturing, model training, the person's device and the tasks done without AI |
| Status | estimated |
All the characteristics
| Identifier | IA-0073, projetcelsius.com/en/ai/database/IA-0073/ |
|---|---|
| Version | 0.3 of 2 November 2026 |
| Category | Occupations and activities, one person, one year |
| Choice of the selected value | database calculation from the entries this one is made of, at the quantities of the unit |
| Sources | calculation of the database |
| Licence | CC BY 4.0 |
Usage parameters
Advanced settings
Tasks and frequencies
Frequency and share done with AI, task by task · typical use
Adjust each task
| Task | Frequency | Share with AI | Per year | Electricity | Share |
|---|---|---|---|---|---|
| Write an email | 330 | 76 Wh | 6 % | ||
| Quick question | 528 | 160 Wh | 12 % | ||
| Proofread a page | 165 | 99 Wh | 8 % | ||
| Write a page | 44 | 20 Wh | 2 % | ||
| Web search with an AI | 220 | 600 Wh | 47 % | ||
| Conversation of 10 exchanges | 26.4 | 93 Wh | 7 % | ||
| Write meeting minutes | 26.4 | 38 Wh | 3 % | ||
| Analyse a spreadsheet | 19.8 | 55 Wh | 4 % | ||
| Summarise 30 pages | 13.2 | 64 Wh | 5 % | ||
| Translate a page | 26 | 16 Wh | 1 % | ||
| Presentation of 10 slides | 5.5 | 63 Wh | 5 % | ||
| Summarise a meetingdoes not apply with this tooling | |||||
| Automated workflowdoes not apply with this tooling | |||||
| Browser agentdoes not apply with this tooling |
Result for the chosen case
220 days, servers worldwide: 590 gCO2e, 1.3 kWh.
For a carbon footprint
| Item | purchases of services (GHG Protocol scope 3, category 1); regulatory BEGES, category 4 |
|---|---|
| Quantity | 1 role over one year |
| Factor | 590 gCO2e per role over one year, IA-0073 version 0.3, according to the local grid (location-based), servers worldwide |
| Total | 590 gCO2e · not counted: server manufacturing, model training, the person's device and the tasks done without AI |
Typical usage profile
Projet Celsius assumption
The typical office employee hands AI about 7 of their 75 emails, asks it 12 questions, has it rework 4 texts and run 5 web searches; a page, meeting minutes every 2 weeks and a thinking conversation complete the week: about 32 requests per week. Over the year, about 1.3 kWh, of which 47% for AI-assisted web searches, which read whole pages. Light use, 440 Wh; intensive, 4.4 kWh. With AI in the office suite, 3 to 4 meetings per week are summarised without being asked and the year rises to 2.2 kWh; with agents that sort their mailbox, to 2.4 kWh. For this role, the tool provided weighs almost as much as the person's own use.
Breakdown by item
Quantity, unit value, contribution and nature of the data for each item
| Item | Quantity | Unit value | Contribution | Share | Nature of the data |
|---|---|---|---|---|---|
| Web search with an AI | 220 times | 2.7 Wh | 600 Wh | 47 % | assumption |
| Quick question | 530 times | 0.3 Wh | 160 Wh | 12 % | assumption |
| Proofread a page | 170 times | 0.6 Wh | 99 Wh | 8 % | assumption |
| Conversation of 10 exchanges | 26 times | 3.5 Wh | 93 Wh | 7 % | assumption |
| Write an email | 330 times | 0.23 Wh | 76 Wh | 6 % | assumption |
| Summarise 30 pages | 13 times | 4.9 Wh | 64 Wh | 5 % | assumption |
| Presentation of 10 slides | 5.5 times | 11 Wh | 63 Wh | 5 % | assumption |
| Analyse a spreadsheet | 20 times | 2.8 Wh | 55 Wh | 4 % | assumption |
| Write meeting minutes | 26 times | 1.4 Wh | 38 Wh | 3 % | assumption |
| Write a page | 44 times | 0.45 Wh | 20 Wh | 2 % | assumption |
| Translate a page | 26 times | 0.6 Wh | 16 Wh | 1 % | assumption |
Sources of the calculation
18 publications, for this entry and those used to calculate it, with the chosen parameters.
The result is calculated by Projet Celsius. The sources below support its uses, its factors and its conversion; they do not all measure this total.
Uses and assumptions
These publications shed light on AI tasks and uses. They do not measure the carbon footprint of the case calculated.
Apec · June 2025
Role and limits : Apec
What the publication covers. frequency of generative AI use at work, 2,000 private-sector managers surveyed in March 2025
What it does not cover. non-managers and the number of requests
Reading by the database. Recent French survey on frequency of use at work; copy read on a specialist press site
Published observation on uses
Scope of the figure used. Private-sector managers who use generative AI at work every day or almost, March 2025
Limit. Support for the typical level: the median manager user was weekly in early 2025
Published observation on uses
Scope of the figure used. Managers who use it at least once a week without being daily, March 2025
Limit. Support for the typical level: the median manager user was weekly in early 2025
Published observation on uses
Scope of the figure used. Managers who use it at least once a month without being weekly, March 2025
Limit. Support for the typical level: the median manager user was weekly in early 2025
Published observation on uses
Scope of the figure used. Managers who never use it, March 2025 (13% less than once a month)
Limit. Support for the typical level: the median manager user was weekly in early 2025
Support for the "Typical use" setting
Scope of the figure used. The median user among those who use AI, end of 2026: they turn to it every working day for part of their questions, writing and reading, about 5 to 15 requests per day in an office job. In an organisation or a territory, it also sets the share of employees who use it (25%).
Limit. This setting remains an assumption of the database, not a measurement of the occupation.
Crédoc · 2026
Role and limits : Crédoc
Author. Crédoc for Arcep, Arcom, the CGE and the ANCT
What the publication covers. use of generative AI by people aged 12 and over in France in 2025: who, for what, how often, paid or not
What it does not cover. the number of requests
Reading by the database. The reference survey in France, 4,145 people
Published observation on uses
Scope of the figure used. Users who turn first to generative AI to look for information (55% through a search engine)
Limit. Survey of 2025 published in 2026, 4,145 people; support for the frequencies and the use
Published observation on uses
Scope of the figure used. Working people who use generative AI in a professional setting (42% of French people for personal use)
Limit. Survey of 2025 published in 2026, 4,145 people; support for the frequencies and the use
Office employee, 1 year → : Quick question →
About 8 information needs per day; share done with AI based on the share of users who turn to an AI first to look something up (Crédoc, Baromètre du numérique, 2026 edition, 2025 data), adjusted to the occupation.
Scope of the figure used. computation of the response, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
EnronSent
Role and limits : EnronSent
Author. Will Styler
What the publication covers. 96,106 messages sent by Enron employees, 13,810,266 words
What it does not cover. the messages received
Reading by the database. Professional emails from 2000 and 2001: an order of magnitude, not a norm
Write an email → : Write, with reasoning →
The email written, 150 words
Scope of the figure used. server, excluding the data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Write an email → : Read 1,000 tokens →
Length convention: an email
Scope of the figure used. A typical email: the sent messages of the Enron corpus average about 144 words; figure read in an excerpt, notice not verified
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Write an email → : Write, with reasoning →
Length convention: an email
Scope of the figure used. A typical email: the sent messages of the Enron corpus average about 144 words; figure read in an excerpt, notice not verified
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Epoch AI · February 2025
Role and limits : Epoch AI
What the publication covers. the calculation from the hardware and the assumed size of the model
What it does not cover. actual measurements
Reading by the database. A public, transparent calculation
Reference of the selected value
Scope of the figure used. GPT-4o, 500 tokens written, full scope
Limit. A public calculation, from the hardware and the assumed size of the model
Conversation of 10 exchanges → : Re-read from cache →
The context re-read from cache at each turn: the previous exchanges, and the document already in the conversation if there is one
Scope of the figure used. server, convention C4
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Eurostat · June 2026
Individuals - use of generative AI tools (isoc_ai_iaiu) ↗ (new tab)
Role and limits : Eurostat
Author. Eurostat, survey on ICT use in households
What the publication covers. share of people aged 16 to 74 who used generative AI in the last 3 months, at work, privately, for studies, by country and category, 2025 data
What it does not cover. the frequency of use
Reading by the database. Harmonised European survey, first year
Published observation on uses
Scope of the figure used. France, 16 to 74 years, used generative AI at work in the last 3 months, 2025 (European Union: 15.4%)
Limit. Share of users, which depends on the organisation and the population; here, support for the gap between occupations
Published observation on uses
Scope of the figure used. France, employed and unemployed people, used generative AI at work in the last 3 months, 2025
Limit. Share of users, which depends on the organisation and the population; here, support for the gap between occupations
The 6 other publications in this section
Microsoft Research · July 2025
Working with AI: Measuring the Applicability of Generative AI to Occupations ↗ (new tab)
Role and limits
Author. Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri (Microsoft)
What the publication covers. 200,000 conversations with Bing Copilot matched to O*NET activities; applicability score by occupation, in open data (version 7 of October 2026)
What it does not cover. actual adoption by occupation
Reading by the database. Measures what AI is used for and succeeds at, not who uses it
Published observation on uses
Scope of the figure used. Generative AI applicability score for office employees (code 43-9061), 200,000 conversations with Bing Copilot, from 0 to 1
Limit. Used for the occupation coefficient: square root of the score divided by 0.25, the score of office employees
Published observation on uses
Scope of the figure used. Generative AI applicability score for interpreters and translators (code 27-3091), 200,000 conversations with Bing Copilot, from 0 to 1
Limit. Used for the occupation coefficient: square root of the score divided by 0.25, the score of office employees
Office employee, 1 year → : Write a page →
About 1 page written per day (note, letter, procedure), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Office employee, 1 year → : Web search with an AI →
About 5 web searches per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Scope of the figure used. searches, pages read, response, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Office employee, 1 year → : Conversation of 10 exchanges →
About 3 reflection sessions per week (preparing a decision, unblocking a file), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Scope of the figure used. messages, context re-read from cache, responses, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Office employee, 1 year → : Write meeting minutes →
About 2 sets of minutes per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Scope of the figure used. simple tasks, data centre included
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Office employee, 1 year → : Summarise 30 pages →
About 1 long document to read per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Scope of the figure used. document read, summary written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Office employee, 1 year → : Translate a page →
About 1 page to translate per week, assumption; share done with AI set high, translation being among the tasks most often delegated to AI (Microsoft, 2025).
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Office employee, 1 year → : 10-slide presentation →
About 2 presentations per month, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Scope of the figure used. simple tasks, data centre included
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Role and limits
Author. National Center for O*NET Development, US Department of Labor
What the publication covers. tasks of each occupation and frequency reported by job holders, in 7 classes from "once a year or less" to "every hour or more"
What it does not cover. occupations without a frequency survey, such as data scientists
Reading by the database. The reference classification of tasks; US job holders
Published observation on uses
Scope of the figure used. office employees: opening, sorting mail and answering correspondence, share who do it daily or more
Limit. Support for the frequency of the task in the basket; US job holders
Office employee, 1 year → : Write an email →
About 15 emails written per day, correspondence being a daily task for office workers in O*NET; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
OpenAI · September 2025
How People Use ChatGPT (NBER Working Paper 34255) ↗ (new tab)
Role and limits
Author. Aaron Chatterji, Tom Cunningham, David Deming, Zoë Hitzig, Christopher Ong, Carl Shan, Kevin Wadman (OpenAI, Duke, Harvard)
What the publication covers. exact message volumes of the consumer plans; classification of a sample of about 1.1 million conversations by topic, intent and link to work
What it does not cover. enterprise plans, under-18s, logged-out users
Reading by the database. The only published measurement of the volumes and uses of a major assistant; internal data, automatic classification
Published observation on uses
Scope of the figure used. Share of work-related messages, consumer plans, June 2025 (47% a year earlier)
Limit. Used to check the average across occupations
Published observation on uses
Scope of the figure used. Share of writing in work-related messages, July 2025
Limit. Support for the common grid: writing is the first use at work
Published observation on uses
Scope of the figure used. About two thirds of writing requests modify a text supplied (rework, critique, translation, summary)
Limit. Support for the high use for reworking, translating and summarising, lower for writing from scratch
Office employee, 1 year → : Proofread a page →
About 3 texts to rework per day; share done with AI based on the share of work messages that ask for a supplied text to be edited (OpenAI, 2025), adjusted to the occupation.
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Office employee, 1 year → : Analyse a spreadsheet →
About 3 spreadsheets to analyse per week, assumption; share done with AI set low, data analysis carrying little weight in messages (OpenAI, 2025), adjusted to the occupation.
Scope of the figure used. tokens read and written, data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
OpenAI · December 2025
Role and limits
What the publication covers. actual use by ChatGPT enterprise customers and a survey of 9,000 employees
What it does not cover. the consumer plans and any absolute volume per employee
Reading by the database. Publisher report; the gaps between users are the useful data
Published observation on uses
Scope of the figure used. Messages of the most active employees (95th percentile) relative to the median employee, enterprise customers
Limit. Support for the gaps between the light, typical and intensive levels
Projet Celsius · November 2026
Tokens per word, per line of code and per cell, measured with 7 public tokenisers
Role and limits
What the publication covers. 479,888 words from 196 blog articles and their English translation, code from the site and the Python library, a table of measurements; tokenisers of GPT-4o, GPT-4, Llama 3, Gemini (via Gemma 3), Mistral, DeepSeek and Qwen
What it does not cover. the Claude tokeniser, not public
Reading by the database. A measurement on technical texts, figures included; to be repeated if publishers change their tokeniser
Projet Celsius measurement, repeated on request with the public tokenisers
Proofread a page → : Read 1,000 tokens →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Proofread a page → : Write, with reasoning →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Write a page → : Write, with reasoning →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Conversation of 10 exchanges → : Re-read from cache →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Analyse a spreadsheet → : Read 1,000 tokens →
The spreadsheet, 200 rows and 10 columns, and the question
Scope of the figure used. server, excluding the data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Analyse a spreadsheet → : Read 1,000 tokens →
Length convention: a spreadsheet of 200 rows
Scope of the figure used. A table of 200 rows and 10 columns of numbers and short labels: 4.7 tokens per cell, measured on a real table
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Summarise 30 pages → : Read 1,000 tokens →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Summarise 30 pages → : Write, with reasoning →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Translate a page → : Write, with reasoning →
The translated page, counted as a French page
Scope of the figure used. server, excluding the data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Translate a page → : Read 1,000 tokens →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Translate a page → : Write, with reasoning →
Length convention: one page
Scope of the figure used. A report page, headings and paragraphs included: between the 1,500-character sheet (250 words) and a full A4 page in single spacing (about 550 words, at 6.1 characters per word measured on our articles)
Limit. This publication supports the quantity of text or content processed, not its carbon footprint.
Zhang and Nam · June 2026
HANSEL: Extracting Breadcrumbs from Web Agent Trajectories for Interactive Verification ↗ (new tab)
Role and limits
Author. Yujin Zhang and Daye Nam
What the publication covers. 45 web agent trajectories: 13.16 steps and 6.02 pages visited per task on average
What it does not cover. electricity
Reading by the database. A small sample, which gives the order of magnitude of an agent's steps
Web search with an AI → : Read 1,000 tokens →
The 5 pages found, read by the model, and the question: web agents visit 6 pages per task on average
Scope of the figure used. server, excluding the data centre
Limit. The quantity and the share entrusted to AI are database assumptions, not a measured footprint.
Energy and factors
The references of the selected values and of the entries used in this calculation.
Role and limits : Google
What the publication covers. chips, host, standby machines, data centre, on-site water
What it does not cover. manufacturing, training
Reading by the database. The only production measurement published by a provider
Reference of the selected value
Scope of the figure used. Median of a text prompt; chips, host, standby machines, data centre
Limit. The only production measurement published by a provider, not audited by a third party
Google · January 2009
Role and limits : Google
What the publication covers. a search, data centre
Reading by the database. Never remeasured
Reference of the selected value
Scope of the figure used. A search, data centre included
Limit. Never publicly remeasured; Google's AI answers are not published
Luccioni et al. · October 2024
Role and limits : Luccioni et al.
Author. Luccioni, Jernite and Strubell
What the publication covers. GPU, processor, memory
What it does not cover. data centre
Reading by the database. Open models only
Create an image → : Generate an image →
One image, of the chosen size: standard by default
Scope of the figure used. GPU, processor, memory; excluding the data centre
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Reference for the variant "Standard image, from 512 to 1,024 pixels per side"
Scope of the figure used. Average of the open models tested, 1,000 images; median 1.35 Wh, maximum 11.49 Wh
Limit. Open models only; closed generators publish nothing
Microsoft Research · 2026
Estimation de l'énergie des requêtes en production (Joule) ↗ (new tab)
Role and limits : Microsoft Research
Author. Microsoft Research, Oviedo et al.
What the publication covers. H100 nodes, production
Reading by the database. Calibrated on production
Reference of the selected value
Scope of the figure used. Median, 500 tokens read and 300 written, H100 nodes
Limit. Not a measurement, but calibrated on production; judges public estimates to be 4 to 20 times too high
OpenAI · June 2025
Role and limits : OpenAI
Author. Sam Altman, OpenAI
What the publication covers. an average query
What it does not cover. method, scope
Reading by the database. One sentence, no method
Reference of the selected value
Scope of the figure used. "An average query", with no published method
Limit. No published scope; consistent with Google
The other publication in this section
Watershed · Aug. 2026
Estimating GHG Emissions from AI Use: Framework for Corporate-Level Measurement ↗ (new tab)
Role and limits
Author. Bistline, Ulissi, Davis, Glidden, Joyce, Li, Mohsenin and Suh (Watershed)
What the publication covers. factors per 1,000 tokens read and written at the server before PUE (host and standby machines included), PUE separate (1.10 for a large operator, 1.56 for an unknown operator)
What it does not cover. clean measurements, the model and the hardware
Reading by the database. Default factors proposed for review (public data as of June 2026); for a large operator, taken from Google's measurement published in 2025 (Elsworth et al.), which already contains the data centre overhead; constant per token, they underestimate long contexts and agents according to the authors
Reference for the variant "Medium model, Watershed default factor"
Scope of the figure used. Default factor for 1,000 tokens read, medium model, large operator, at the server before PUE (Watershed table 2, public data as of June 2026)
Limit. A default value that Watershed derives from Google's measurement (0.24 Wh for the median text query of the Gemini app), with about 500 tokens per query and a written token costing 3 times a read token; Watershed does not give the split between read and written tokens. The 0.24 Wh already include the overhead of Google's data centre (0.02 Wh, 8%): with 375 tokens read and 125 written, the simplest reading, the factor contains it, and the PUE of 1.10 applied afterwards counts it a second time (about 9%). Values proposed for review, final at the next version of the framework
Reference for the variant "Medium model, Watershed default factor"
Scope of the figure used. Default factor for 1,000 tokens written, medium model, large operator, at the server before PUE (Watershed table 2, public data as of June 2026)
Limit. 3 times the reading factor, based on the ratio between writing and reading selected by Watershed (Patel et al., Splitwise, 2024). Same derivation and same caveat as for reading: Google's 0.24 Wh already include about 8% of data centre. Values proposed for review, final at the next version of the framework
Write an email → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Reference of the selected value
Scope of the figure used. Default factor for a large operator, from the server to the data centre; 1.56 for an unknown operator
Limit. A default value, not a measurement
Quick question → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Proofread a page → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Write a page → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Web search with an AI → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Conversation of 10 exchanges → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Analyse a spreadsheet → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Summarise 30 pages → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Translate a page → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Create an image → : Data centre PUE →
The data centre, convention C3
Scope of the figure used. building cooling and losses
Limit. This figure covers part of the calculation; it does not measure the whole entry.
Conversion into carbon
The source of the electricity factor of the selected location.
Worldwide: 458 gCO2e/kWh, life cycle.
Role and limits : Ember
Author. Ember, Our World in Data
What the publication covers. electricity generation over the life cycle, by country
What it does not cover. guarantees of origin
Reading by the database. The same source for all countries
Conversion of electricity into carbon: worldwide
Scope of the figure used. the production of electricity over the life cycle, including power plant construction and fuels
Limit. An average for the chosen country, not the electricity contract or the data centre of a specific service.
Nature of the data
Published readings, conventions, assumptions
- Projet Celsius assumptions 100 %
Usage assumptions
Frequency, share done with AI according to adoption, source of each assumption
Write an email : 15 per day, 10% with AI in typical use
About 15 emails written per day, correspondence being a daily task for office workers in O*NET; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Quick question : 8 per day, 30% with AI in typical use
About 8 information needs per day; share done with AI based on the share of users who turn to an AI first to look something up (Crédoc, Baromètre du numérique, 2026 edition, 2025 data), adjusted to the occupation.
Proofread a page : 3 per day, 25% with AI in typical use
About 3 texts to rework per day; share done with AI based on the share of work messages that ask for a supplied text to be edited (OpenAI, 2025), adjusted to the occupation.
Write a page : 1 per day, 20% with AI in typical use
About 1 page written per day (note, letter, procedure), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Web search with an AI : 5 per day, 20% with AI in typical use
About 5 web searches per day, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Conversation of 10 exchanges : 3 per week, 20% with AI in typical use
About 3 reflection sessions per week (preparing a decision, unblocking a file), assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Write meeting minutes : 2 per week, 30% with AI in typical use
About 2 sets of minutes per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Analyse a spreadsheet : 3 per week, 15% with AI in typical use
About 3 spreadsheets to analyse per week, assumption; share done with AI set low, data analysis carrying little weight in messages (OpenAI, 2025), adjusted to the occupation.
Summarise 30 pages : 1 per week, 30% with AI in typical use
About 1 long document to read per week, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Translate a page : 1 per week, 59% with AI in typical use
About 1 page to translate per week, assumption; share done with AI set high, translation being among the tasks most often delegated to AI (Microsoft, 2025).
Presentation of 10 slides : 22 per year, 25% with AI in typical use
About 2 presentations per month, assumption; share done with AI taken from the value common to all occupations for this task, then adjusted to the occupation using the Microsoft study of 200,000 conversations.
Summarise a meeting : 1 per week, 70% with AI in typical use
About 5 meetings per week, assumption; with AI in the office suite, most are transcribed and summarised without being asked, assumption; one summarised meeting serves its 5 participants, each carrying a fifth of it.
Automated workflow : 40 per day, 30% with AI in typical use
About 40 emails received per day, assumption; with agents, a share is sorted and prepared by an automated workflow, assumption with no published survey.
Browser agent : 5 per week, 20% with AI in typical use
About 5 online procedures per week (form, booking, statement), assumption; share delegated to an agent set without a published survey.
Published figures in support
OpenAI, Chatterji et al. (2025): 27% of messages
Share of work-related messages, consumer plans, June 2025 (47% a year earlier)
OpenAI, Chatterji et al. (2025): 40% of work messages
Share of writing in work-related messages, July 2025
OpenAI, Chatterji et al. (2025): 67% of writing messages
About two thirds of writing requests modify a text supplied (rework, critique, translation, summary)
OpenAI (2025): 6 times the median
Messages of the most active employees (95th percentile) relative to the median employee, enterprise customers
Microsoft Research, Tomlinson et al. (2026): 0.242 applicability score
Generative AI applicability score for office employees (code 43-9061), 200,000 conversations with Bing Copilot, from 0 to 1
Microsoft Research, Tomlinson et al. (2026): 0.492 applicability score
Generative AI applicability score for interpreters and translators (code 27-3091), 200,000 conversations with Bing Copilot, from 0 to 1
O*NET (2025): 72% of job holders
office employees: opening, sorting mail and answering correspondence, share who do it daily or more
Eurostat (2025): 18.4% of people
France, 16 to 74 years, used generative AI at work in the last 3 months, 2025 (European Union: 15.4%)
Eurostat (2025): 24.1% of people
France, employed and unemployed people, used generative AI at work in the last 3 months, 2025
Crédoc (2025) : 26 %
Users who turn first to generative AI to look for information (55% through a search engine)
Crédoc (2025) : 30 %
Working people who use generative AI in a professional setting (42% of French people for personal use)
Apec (2025): 12% of managers
Private-sector managers who use generative AI at work every day or almost, March 2025
Apec (2025): 23% of managers
Managers who use it at least once a week without being daily, March 2025
Apec (2025): 14% of managers
Managers who use it at least once a month without being weekly, March 2025
Apec (2025): 38% of managers
Managers who never use it, March 2025 (13% less than once a month)
Methodology conventions
With their alternatives
| The working day and year | A working year counts 220 days and 44 weeks, a personal year 365 days; the task lists of occupations give, as a median, about 2 requests to AI per working day at low adoption, 6 at standard adoption, 21 at intensive adoption, excluding code suggestions |
|---|---|
| The default location | Without other information, the servers are in the world, at the world average electricity |
| Electricity, location-based, on a life cycle basis | The kilowatt-hour is that of the servers' country, on a life cycle basis, from a single source for all countries |
| Two significant figures | A calculated value is rounded to two significant figures, a published value is reproduced with the precision of its source; the calculation keeps full precision |
| The formula | the sum of what makes up the entry, then CO2e: electricity (Wh) multiplied by the carbon intensity of the server location (gCO2e/kWh), divided by 1,000 |
| Calculate a case without an entry | The formula, the default values and a step-by-step example |
Uses in the hub
Charts, figures, analyses and articles that cite this entry
History
Entry in version 0.3 of 2 November 2026, in the database in version 0.4
- 2 November 2026Designation : An enterprise licence, one year becomes Enterprise licence, 1 year.More precise designation: the object first, the specification after the comma, the quantity or period at the end; the former title remains searchable.
- 2 November 2026Designation : Enterprise licence, 1 year becomes Office employee, 1 year.Database overhaul into tiers: the block becomes the office employee's task basket; the former enterprise licence moves to the Microsoft 365 Copilot licence (IA-0326) and to organisation services.
- 2 November 2026Short title : Enterprise licence, 1 year becomes Office employee, 1 year.Database overhaul into tiers: the block becomes the office employee's task basket; the former enterprise licence moves to the Microsoft 365 Copilot licence (IA-0326) and to organisation services.
- 2 November 2026Address : licence-d-entreprise-1-an devient salarie-de-bureau-1-an.Database overhaul into tiers: the block becomes the office employee's task basket; the former enterprise licence moves to the Microsoft 365 Copilot licence (IA-0326) and to organisation services; the old address now redirects.
- 2 November 2026Functional unit : 1 licence · an devient 1 poste · an.Database overhaul into tiers: the block becomes the office employee's task basket; the former enterprise licence moves to the Microsoft 365 Copilot licence (IA-0326) and to organisation services.
- 31 October 2026Creation of the database, in its first version (0.1).
